Full-screen keyboard energy consumption management method

Abstract

The present application relates to human-computer interaction display technical field, especially to a kind of full screen keyboard energy consumption management method, collection sight, fingertip and ambient light data and write into event queue;Using silicon photonic interference network and oxide memory resistance array inference obtains gaze position estimation and keystroke hot zone probability, high-dimensional super coding fusion both and combine real-time available power, in the micro-optimizer generation area brightness matrix and power instruction;According to brightness matrix write phase adjustable holographic waveguide drive light-emitting pixel array, and switch refresh frequency and backlight intensity according to power instruction;When mutual information is lower than threshold value and power margin is sufficient, time delay dependent plasticity update is executed, and array is closed when idle or low power, and character is kept by bistable cholesteric liquid crystal.The present application realizes millisecond level brightness self-adaptation and standby near zero power consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of human-computer interaction display, and in particular to a full-screen keyboard energy consumption management method. BACKGROUND

[0002] The full-screen keyboard embeds the display panel into the keycap plane, and can dynamically present multi-language layout, shortcut icons and game macros. With the sharp increase in pixel density, backlight and driving logic become the main power consumption source. Without special energy consumption management, the keyboard temperature rises, the battery life drops, and it is difficult to deploy in laptops, mobile workstations and gaming peripherals, causing a bottleneck in industry popularization.

[0003] The existing scheme mostly adopts fixed backlight partitioning or key-by-key pulse width modulation: the partitioned brightness table is used to reduce the brightness of non-input areas, but the partitioning granularity is coarse, and it cannot be adjusted in real time with the change of the line of sight, still wasting about 40% power. The key-by-key LED pulse width modulation is used, and the central processing unit is polled and refreshed, which is limited by the bandwidth of the serial bus, and the refresh delay is greater than or equal to 20ms; in the high-frequency mode, the constant brightness of all keys must be maintained, causing unnecessary driving current. The hot area is predicted and dimmed using a pure software model, but floating point operations are required for each frame, which has large delay and high power consumption, and still triggers calculation peaks when the power is tight. The above methods are difficult to reduce display power consumption and heat due to "low spatial resolution", "high time sequence overhead" and "lack of power constraints". SUMMARY

[0004] In view of the many problems existing in the prior art, the present application provides a full-screen keyboard energy consumption management method. The present application first collects the line of sight, fingertip and illumination data, and obtains the gaze and hot area weight through photonic-memristor hardware reasoning; after high-dimensional coding of the weight, the brightness matrix and power instruction are generated according to the available power in the differentiable optimizer; the waveguide phase mask drives the light-emitting pixel array, and switches to low frequency or off when the power is insufficient or idle, and the cholesterol liquid crystal maintains the characters with zero power consumption, thereby realizing dynamic energy-saving display in milliseconds.

[0005] A full-screen keyboard energy consumption management method, comprising the following steps:

[0006] Collecting line of sight images to generate line of sight vector data, collecting fingertip position information to generate fingertip proximity event data, and collecting environmental light information to generate environmental light intensity data, and packaging the three types of data into event data frames and writing them into an event queue;

[0007] Performing photonic feature compression and memristor pulse reasoning on the event data frames to obtain gaze position estimation data and keystroke hot area probability vectors, and generating regional brightness matrix data and power instructions in the differentiable optimization process by fusing the two types of data through high-dimensional super coding and combining the available power of the keyboard;

[0008] According to the regional brightness matrix data, a programmable optical waveguide writes a phase mask to drive a light-emitting pixel array, and adjusts the refresh frequency and backlight intensity of the light-emitting pixel array according to the power instruction; the memristor weight is updated when the mutual information meets the condition, and the refresh frequency is reduced or the light-emitting pixel array is turned off if there is no event for a long time or the available power is lower than the threshold, so that the character display is maintained by the bistable cholesteric liquid crystal display layer.

[0009] Preferably, the photonic feature compression is completed by a silicon photonic interference network to perform matrix convolution and integration in the optical domain to output compressed features.

[0010] Preferably, the memristor pulse inference is performed in a cross-structure oxide memristor array, and a discharge is triggered to complete weight summation when the pulse accumulation voltage reaches a threshold.

[0011] Preferably, the high-dimensional super coding fusion maps the gaze position estimation data and the keystroke hot zone probability vector into orthogonal binary vectors respectively, and generates a super coding feature vector by element-level multiplication superposition.

[0012] Preferably, the power instruction is generated by gradient descent iteration with the weighted sum of the regional brightness matrix data as the target and the available power of the keyboard as the only constraint.

[0013] Preferably, the available power of the keyboard is calculated in real time by the product of the power supply current and the power supply voltage.

[0014] Preferably, the programmable optical waveguide is a phase-adjustable holographic waveguide, and a phase mask written by an electronic signal controls the coupling of a light beam to a target key area.

[0015] Preferably, the light-emitting pixel array has two levels of high refresh frequency and low refresh frequency, and switches between the two levels according to the power instruction.

[0016] Preferably, when the mutual information is lower than a preset threshold and the available power of the keyboard is higher than a preset threshold, a time-delay dependent plasticity update is performed on the memristor weight.

[0017] Preferably, when no event data frame is received within a preset time or the available power of the keyboard is lower than a preset threshold, the light-emitting pixel array is reduced to a low refresh frequency or turned off, and the character display is maintained by the bistable cholesteric liquid crystal display layer.

[0018] Compared with the prior art, the application has the following advantages and beneficial effects:

[0019] The event features are compressed by a silicon photonic interference network, and sub-microsecond parallel multiplication and addition are realized.

[0020] The pulse inference is performed by a cross-structure oxide memristor array, and millisecond-level gaze-hot zone recognition is realized.

[0021] Through high-dimensional hyper-encoding fusion, sparse weight generation is realized, and integer domain optimization is realized.

[0022] Through the differentiable optimizer and power monitoring closed loop, the brightness matrix distribution according to the power constraint is realized.

[0023] Through the two-stage refresh and cholesteric liquid crystal static retention, high-speed interaction and zero-power standby are realized. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The flowchart of the method of the present application is shown in the figure.

[0025] Figure 2 The core interaction diagram in the present application is shown in the figure. DETAILED DESCRIPTION

[0026] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments can be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present disclosure.

[0027] The terms used herein are merely for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The terms "include", "contain" and the like used herein indicate the presence of the described features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted to have meanings consistent with the context of the present specification, and should not be interpreted in an idealized or overly formal manner.

[0029] As shown in Figure 1 - Figure 2 A full-screen keyboard energy consumption management method includes the following steps:

[0030] Collecting line of sight images to generate line of sight vector data, collecting fingertip position information to generate fingertip proximity event data, collecting ambient light information to generate ambient light intensity data, and encapsulating the three types of data as event data frames and writing them into an event queue.

[0031] In the first step of the full-screen keyboard energy consumption management method, the system needs to collect line of sight images, fingertip position information and ambient light information in real time, and encapsulate the three types of data as event data frames and write them into an event queue.

[0032] Gaze image acquisition and gaze vector data generation. An integrated infrared photodetector array is set in front of the keyboard. A narrowband optical filter is used to shield visible light scattering and only keep the infrared pupil reflection signal. The output grayscale frame of the detector array then enters a convolution-threshold pipeline. The convolution kernel is five by five in size, and the weights are determined by offline training to enhance the pupil edge features. The threshold module selects an adaptive threshold, which is set to the image mean minus three times the standard deviation to eliminate background noise. After filling in the pupil outline through morphological closing operation, the elliptical geometric center is extracted using elliptical fitting. To map the pixel coordinates to the three-dimensional coordinate system of the keyboard, the system completes camera calibration in advance to obtain the intrinsic and extrinsic matrices. The pixel coordinate column vector Left multiply the calibration matrix and normalize, and the gaze direction vector is obtained .

[0033] wherein denotes the calibration matrix, denotes the pixel coordinate column vector, denotes the Euclidean norm. This operation is completed on a digital signal processor with a delay of less than two milliseconds. The gaze vector data can represent the projection position of the user's gaze point on the keyboard plane, and is an important weight factor for the subsequent brightness matrix calculation.

[0034] Finger position information acquisition and finger proximity event data generation. A rectangular Hall sensor array is arranged under the keycap, with each Hall element corresponding to a logical key position, used to sense the perturbation of the magnetic field caused by the keycap metal sheet. When the fingertip is pressed down, the Hall voltage output changes linearly; at the same time, a three-axis inertial measurement unit is embedded in the keyboard shell to record the acceleration of the fingertip movement. The system uses an extended Kalman filter to input the Hall voltage sequence and acceleration sequence into the observation equation, and outputs the instantaneous displacement and velocity of the fingertip. If the estimated vertical distance of the filter is lower than the set threshold and the velocity is lower than the preset velocity threshold, it is determined that the finger proximity event is triggered, and the key position index and timestamp are recorded to generate the finger proximity event data. The finger proximity event data is used for prior correction of the hot area probability in high-dimensional super coding, reducing the reasoning burden of the photon network.

[0035] Ambient light information acquisition and ambient light intensity data generation. A multi-channel light sensor is set at the bottom of the display area, with each channel corresponding to the blue, green, and infrared wavebands. The system uses a five-time sliding average filter to reduce power frequency flicker noise, and then performs weighted summation on the three-channel light intensity to obtain the ambient illumination. The weights are set according to the channel response curve normalization. If the ambient illumination is higher than the upper brightness threshold, the subsequent micro-optimizer automatically reduces the backlight weight when calculating the brightness matrix, and vice versa. The ambient light intensity data ensures that the keyboard maintains a relatively constant visual contrast under different lighting conditions.

[0036] Event data frame encapsulation and writing. The system writes gaze vector data, fingertip proximity event data, and ambient light intensity data into a fixed-format data frame in ascending order of timestamps. The data frame structure consists of three parts: header, payload, and checksum. The header records the frame number and generation time; the payload contains fixed-length fields for three sets of data; the checksum field uses a cyclic redundancy check (CRC) code to verify frame integrity. After encapsulation, the data frame is written to a circular event queue via the on-chip bus. The event queue adopts a lock-free circular buffer structure, which can concurrently support multiple producers and consumers, achieving decoupling of the gaze processing stream, fingertip processing stream, and ambient light processing stream.

[0037] Based on the above design principles, the first step implements multi-channel synchronous sampling at the hardware level, and then fuses data from different modalities into a unified data frame at the algorithm level. This not only ensures time consistency but also provides a time-continuous data stream for subsequent photonic feature compression and memristor inference. Experimental results show that at kilohertz-level event rates, the encapsulation latency is no more than one millisecond, queue reading is non-blocking, and the real-time power management requirements of high refresh rate keyboards are met.

[0038] Photon feature compression and memristor pulse inference are performed on the event data frame to obtain gaze position estimation data and keystroke hot zone probability vector. The two types of data are fused by high-dimensional supercoding and combined with the available keyboard power to generate regional brightness matrix data and power instructions in the differentiable optimization process.

[0039] After the gaze vector data, fingertip proximity event data, and ambient light intensity data are written into the event queue, the system enters the core processing stage of this invention: first, feature compression is completed in the optical domain, then pulse inference is performed in the memristor array, then the two inference results are fused using a high-dimensional super-encoding method, and regional brightness matrix data and power commands are output during the differentiable optimization process.

[0040] The principle of optical domain feature compression. When an event data frame arrives, the control logic injects 32 optical modulation signals into a rectangular silicon photonic interference network according to a fixed address table. The basic unit of the network is a Mach-Zehnder interferometer, which consists of two couplers and two phase shifters. The input signal is split by the first coupler, the phase shifters introduce a programmed phase difference, and the output coupler then superimposes the two beams. For each input optical signal... ( (channel index), corresponding weight matrix of the interferometric network The The output is accumulated through a chain coupler to obtain a compressed light intensity vector. :

[0041]

[0042] In the formula Indicates the first The output channel and the first Optical weights between input channels This indicates the total number of input channels. This invention uses an electronically controlled phase shifter to set this. The corresponding phase difference has a write time of no more than 5 microseconds. The optical domain convolution-integration delay scales approximately linearly with the number of channels. At that time, the measured convolution-integration delay was less than 200 picoseconds. Compared with traditional electronic matrix multiplication, optical domain multiplication-addition can reduce the delay by two orders of magnitude and significantly reduce dynamic power consumption.

[0043] Memristor pulse inference principle. A compressed light intensity vector enters an avalanche photodiode array and is converted into a current pulse. The pulse is then limited and converted into a square wave before being injected into a 128×128 cross-structure memristor array. Each memristor cell exhibits variable conductance during the write phase and remains non-volatile during the read phase. The pulse collision weighted summation follows an integral-threshold mechanism, accumulating pulse charge on the column lines, and the column line voltage... Integral growth as follows:

[0044]

[0045] in For the first Line 1 List the conductance of the memristor. For column line capacitance. When Reaching the threshold At any given time, the column line triggers a discharge, and the decoding logic records one pulse output. The neuron's output pulse sequence is statistically analyzed over a time window to generate gaze position estimation data and a keystroke hotspot probability vector. Due to complete hardware integration, this process does not depend on a global clock, and the event-triggered power consumption increases linearly with the number of pulses.

[0046] High-dimensional supercoding fusion principle. To simultaneously utilize spatial attention and historical hotspot information, the system assigns a set of orthogonal binary vectors to the gaze position estimation data. Assign another set of orthogonal binary vectors to the keystroke hot zone probability vector. The two sets of vectors are superimposed using element-wise multiplication, and then a 10000-dimensional binary vector is generated using a sign function:

[0047]

[0048] in For the normalized gaze position vector, The keystroke hot zone probability vector is output by the sign function. Binary. High-dimensional sparse vectors preserve independent information channels, facilitating efficient computation in the integer domain.

[0049] The luminance matrix and power instruction can be generated by micro-optimization. The system aims to maximize the weighted luminance of the gaze area under the power constraint of the keyboard. Let the area luminance matrix be , element representing the key position luminance level, and the weight matrix be obtained by mapping. The optimization model is:

[0050]

[0051] The display power is a sub-linear function of the luminance matrix, and the available power is calculated in real time by the keyboard power supply current and voltage. The luminance matrix and power instruction can be obtained by 16 iterations of gradient descent. Since the objective function and constraints are differentiable, they can be directly calculated on tensor core hardware, with a single iteration delay of less than 0.2 milliseconds and a total delay of less than 3 milliseconds.

[0052] Effect comparison. Compared with the pure digital reasoning scheme, the present application reduces the reasoning delay to milliseconds by using photon multiplication and memristor accumulation, reducing the overall delay by about 95%. High-dimensional super-coding reduces weight density, and the optimizer calculates power consumption in the integer domain, which is only about 10% of the floating-point implementation. By adjusting the refresh mode in real time through the power instruction, the system can seamlessly switch between 144 Hz and 24 Hz. In a low light scene of 25 lux, the luminance of the gaze area is increased by 100%, and the total power consumption of the keyboard is reduced by 30%. After the keyboard is idle for 500 milliseconds, it switches to a low-power static mode, the light-emitting pixel array is turned off, and the bistable cholesteryl liquid crystal maintains character display, with static power consumption close to zero.

[0053] Embodiment. Place the keyboard in a dark room with an illuminance of 25 lux, and the user performs 60 seconds of text input. The system automatically increases the backlight brightness after detecting low light. The photon-memristor reasoning displays the gaze key position concentrated in the left-hand main area, and the optimizer allocates higher brightness to the left-hand main area keys. The measured display power accounts for 90% of the available power, and the overall power consumption of the keyboard is about 1.0 watt. After 500 milliseconds of input stop, the static mode is triggered by the empty event queue, and the power consumption drops to 0.01 watts; when the user inputs again, the system restores the high refresh frequency within 120 milliseconds, and there is no obvious flicker in vision. This embodiment verifies that the present application can achieve adaptive energy management in low light, dynamic input, and static scenarios, and has obvious energy saving compared with the conventional backlight constant brightness scheme.

[0054] Preferably, the photon feature compression completes matrix convolution through a silicon photon interference network and performs integration in the optical domain to output compressed features.

[0055] In the full-screen keyboard energy consumption management method of the application, the photon feature compression link undertakes the key task of efficiently mapping the event data frame to a low-dimensional sparse feature space. This link realizes matrix convolution and integration based on a silicon photon interference network, fully utilizes the parallelism of photon reflection and interference, and completes multi-input multi-output operation with extremely low delay, thereby saving a large amount of electronic multiplication and addition operation power consumption for subsequent memristive pulse reasoning.

[0056] The silicon photon interference network is a phase-programmable multi-input multi-output filter matrix distributed on a silicon substrate waveguide and composed of phase shifters and couplers. Each phase shifter changes the effective refractive index by micro-heating or electric carrier injection, thereby adjusting the transmission phase; and the coupler is responsible for energy exchange between adjacent waveguides. Assuming that there are N optical signals at the input end of the waveguide, and M channels are set at the output end of the network, the whole network is equivalent to a complex weight matrix, and the weight elements can be precisely adjusted by phase configuration of the phase shifter. Before entering the photon domain, the event data frame is first converted and modulated into N coherent light intensities , and each intensity encodes a group of numerical features. The network output is the weighted accumulation of and . Since the light intensities are naturally superimposed at the coupler, the output does not need subsequent electrical domain summation to obtain the convolution result. Through delay structure or photodiode energy accumulation, the integration operation can be completed in the optical domain, the instantaneous convolution signal is converted into average light intensity, and the compressed feature vector is output.

[0057]

[0058] In the formula, is the integral window length, represents the light energy integration of the event data frame in the time window; is the complex weight configured by the phase shifter, including amplitude and phase parts. Tuning of corresponds to learning of the implicit matrix convolution kernel; after obtaining the weight table through offline training, the system writes the phase of each phase shifter in the power-on initialization stage, and the convolution kernel can be fixed, but it can still be re-written to realize online reconstruction when it needs to be updated.

[0059] In terms of structure implementation, the interference network adopts a rectangular grid topology, the number N of vertical waveguides and the number M of horizontal waveguides correspond to the input and output dimensions respectively, and full-coupling or weak-coupling waveguide intersections are placed at the grid intersections. The phase shifters are arranged on the straight sections of the waveguides, and the couplers are arranged at the waveguide intersections, and the two are alternately arranged. In order to reduce the insertion loss, the length of each phase shifter is kept at the order of 50 microns, the length of the coupler is kept at the order of 10 microns, and the waveguide width is uniformly 500 nanometers. The overall size of the network can be realized in a 5 square millimeter silicon chip, and it is compatible with CMOS.

[0060] In terms of data stream design, the event data frame is first encoded as an optical intensity vector. The encoding method uses pulse width modulation to map each feature value to the pulse length of the corresponding channel, ensuring that the optical intensity is proportional to the feature value. After encoding, the data is injected into the waveguide through a multi-channel parallel modulator. After the optical signal passes through the rectangular interference network, M compressed optical intensities are obtained. Avalanche photodiodes are placed at the output to convert the optical intensity into a voltage pulse, which is then output to the memristor pulse network. Since the rectangular network is in a passive state throughout the entire process of the input optical signal, its power consumption only comes from the phase shifter holding power and the photodiode bias, and does not increase linearly with the input bandwidth, which is beneficial to meet the energy consumption indicators of high refresh keyboards.

[0061] The weight writing is realized in a digital phase control manner: the system first quantizes the target phase value to an 8-bit code, and then applies a corresponding current to the heating electrode of the phase shifter until the error at the test end of the optical interference is less than 0.02 radians. To address environmental temperature drift, an on-chip temperature sensor samples every 10 milliseconds, and the temperature drift is mapped to the phase shifter correction table to keep the convolution weight stable in a closed loop. To avoid crosstalk between waveguides, isolation grooves are added to the outer periphery of the network, and ground shielding is arranged in the metal layer. Tests show that, in the case of 32 channels and 16 output channels, the average holding power of the phase shifter is less than 5 milliwatts, and the insertion loss is controlled within 2 decibels.

[0062] Photon feature compression maps the original 32-dimensional event features to a 16-dimensional sparse feature vector and completes a matrix integration in the optical domain, reducing the calculation delay and unnecessary switching energy consumption by two orders of magnitude compared to digital multiplication and addition circuits. Compared with the FPGA scheme, the compression delay is reduced from 30 microseconds to 200 picoseconds, and the energy consumption is reduced by 80%. The compressed vector directly drives the memristor inference, which can use a smaller array size and reduce the capacitive burden of the horizontal wires. Hardware simulation shows that the overall throughput of the photon-memristor combined path is 10 times higher than that of the pure electronic path, and the end-to-end inference delay is still less than 3 milliseconds at a refresh rate of 144 hertz.

[0063] Embodiment: The keyboard is configured with 32-line line-of-sight and fingertip channel inputs, and the rectangular interference network uses a 4x8 structure to compress the 32-dimensional input to an 8-dimensional output. After training and optimization, the output features can accurately cover the top 3 key positions of the true gaze probability in 98% of cases. When the network is continuously operated at room temperature of 23 degrees Celsius for 60 minutes, the phase shifter holding power is measured to be 3.8 milliwatts, and the output power stability is maintained within 0.15 decibels. The corresponding keyboard prediction hit rate is improved by 25%, and the total display power consumption is reduced by 33%, proving the actual energy-saving value of photon feature compression in the full-screen keyboard scenario.

[0064] Preferably, the memristor pulse inference is performed in a cross-structure oxide memristor array, and a discharge is triggered to complete weight summation when the pulse accumulation voltage reaches the threshold value.

[0065] The task of the memristive pulse inference to convert the sparse feature vector compressed from photons into gaze position estimation data and keystroke hot region probability vector directly affects the accuracy of subsequent brightness allocation and the timeliness of energy consumption control. The present application selects a cross-structure oxide memristor array to implement a hardware pulse neural network, and the operation mechanism thereof can be summarized as three stages of pulse weighting, line integration and threshold triggering.

[0066] The cross-structure array is composed of orthogonal row lines and column lines, and an oxide memristor is welded at each intersection. The direct current conduction state of the memristor is multi-stage adjustable under the action of a write-in voltage. The row lines inject input pulse sequences, and the column lines form integration nodes by grounding the capacitors and sampling resistors. Let the row direction index be , the column direction index be , the input pulse current of the row line be , the conductance of the memristor be , and the equivalent capacitance of the column line be . The differential expression of the column line node evaluation voltage is:

[0067]

[0068] wherein represents the number of row lines. The upper limit of integration in the formula is determined by the pulse width, and no global clock is needed, only relying on the sequence of event arrival. When accumulates to the threshold , the column line drives an avalanche diode to produce a short-circuit discharge, and the decoding logic captures the discharge event and counts as a neural pulse output. After the discharge, the column line voltage is pulled back to zero, and enters the next integration period. The timing of the row line input pulse is converted from the output light intensity of the photon compression unit by an avalanche photodiode, so the entire network has end-to-end asynchronous pulse compatibility characteristics.

[0069] The hafnium oxide is selected as the active layer of the memristor, the top electrode is connected with the row line, and the bottom electrode is connected with the column line. The device thickness is eight nanometers, and the area is one square micrometer. The write-in operation adopts a current pulse to discretely adjust the conductance to four decimal places, so that the conductance value covers 16 levels to adapt to the probability mapping requirements. The row line and the column line form a 128 row by 128 column matrix through tungsten metal interconnection, and the total effective storage unit is 16384. The column line is connected in series with a sampling resistor of two hundred ohms and an integration network composed of a parallel capacitor of two picofarads. The threshold judgment uses a reference comparator, and the ratio level is set to one point five volts to ensure sensitive discharge triggering without false triggering. When burning weights, the system uses a row-by-row scanning method to apply write-in pulses, and after the write-in is completed, a read-write-calibration cycle is performed for each column to ensure that the error is not more than 3%.

[0070] A frame of compressed feature vector contains 128 pulse currents, each with a pulse width of 10 ns. All the pulses are injected into the row lines in parallel, and the column lines immediately accumulate the charges and generate voltages. For the keyboard scenario, the gaze position output needs to be completed within 10 ms, at which time the column line integration time window is set to 2 ms, and the threshold is discharged immediately once it is reached. The column line discharge event is mapped to the output vector by decoding logic and the probability vector The decoding table hard-codes the exponential soft threshold voting operation, and a short column line discharge interval corresponds to a high probability, and a long interval corresponds to a low probability. This hard-coding avoids floating-point operations and directly completes the attention probability estimation in the pulse domain.

[0071] Due to the parallelism of the row-column cross structure, the array inference delay is basically independent of the row and column size. When the row size is 128 and the column size is 128, the actual measured inference delay is 1.6 ms, and the power consumption is 4.2 mW, which is about 20 times shorter than the delay of a pure digital convolution-full connection network. The non-volatile characteristics of the device make the weights remain unchanged after the array is powered off, and the device can realize fast power-on recovery within seconds. In the experiment, the output stability is less than 0.5% variance when the proportion of injected noise pulses is 10%, which shows that the threshold integration mechanism has high fault tolerance to input disturbance.

[0072] In an embodiment, a 128-row by 128-column memristor array is deployed on a prototype keyboard. The previous photonic unit outputs a 128-dimensional light intensity vector, and the average pulse frequency is 5 MHz. The classical data set of 10,000 frames is input into the array to obtain the actual output gaze position estimation, and the average error is 1.8 key positions. The key hot area probability is compared with the high-resolution ground truth, and the cross entropy is 0.12. Compared with the FPGA version of the convolutional neural network model, the error is increased by less than 5%, but the energy consumption is reduced by 87%. In the long-time running test, the array works continuously for 24 hours, and the weight drift is less than 1.5% on average, and the output error does not show obvious accumulation. This embodiment shows that the memristor pulse inference has significant advantages in balancing the calculation accuracy and low power consumption in the keyboard energy management scenario.

[0073] Through the cross-structure oxide memristor array, the present application realizes hardware-level pulse neural network inference. The pulse accumulation and threshold discharge mechanism moves the multiplication and addition operation to physical charge integration, improves the parallelism, and significantly reduces the time delay. The scheme seamlessly connects with the photonic compression unit to realize an end-to-end event-driven inference link, provides high-confidence gaze and hot area information for subsequent high-dimensional super coding and brightness matrix generation, and finally significantly improves the energy self-adaptive performance of the full-screen keyboard.

[0074] Preferably, the high-dimensional super coding fusion maps the gaze position estimation data and the key hot area probability vector into orthogonal binary vectors respectively and generates a super coding feature vector by element-level multiplication.

[0075] After completing the photon feature compression and memristive impulse inference, the application needs to fuse the two outputs, the gaze position estimation data and the keystroke hot zone probability vector, into a unified and sparse high-dimensional representation, so as to serve as a weight matrix and drive the keyboard brightness allocation in the subsequent differentiable optimization process. The traditional method usually directly splices or weighted averages the two vectors, but this way is easy to introduce information redundancy and difficult to ensure numerical stability. The high-dimensional super coding fusion method proposed by the application has three advantages in the event-driven keyboard scene: first, the spatial attention information and the long-term hot zone statistical information are mapped to mutually orthogonal subspaces, avoiding weight interference; second, the output vector dimension is fixed, facilitating hardware storage and subsequent tensor operations; third, the vector elements are only ±1 binary, which can be completed in the integer domain, reducing power consumption.

[0076] At the principle level, the system first allocates a set of orthogonal binary random vectors to the gaze position estimation data; hereinafter referred to as gaze coding basis vectors. The gaze position can be discretized to N x M key coordinates on the keyboard plane, so a total of N x M basis vectors are needed. The system generates a 10000-dimensional binary sequence through a linear feedback shift register in the power-on stage, stops when the sequence length reaches N x M, and obtains the gaze coding basis matrix . For the keystroke hot zone probability vector, since the hot zone probability can be normalized to the range of 0 to 1, an orthogonal binary vector is allocated to each key position, forming the hot zone coding basis matrix . The gaze coding basis vectors and the hot zone coding basis vectors use different seeds when generated to ensure overall orthogonality. Orthogonality in the binary space is defined as the vector dot product being approximately zero, that is:

[0077]

[0078] where represents the th gaze coding basis vector, represents the th hot zone coding basis vector.

[0079] The gaze position estimation data is a key probability distribution, and the system uses the maximum probability key index as the gaze center; the keystroke hot zone probability vector directly uses the normalized probability value . In order to make the probability influence vector amplitude, the system threshold binaryzes the hot zone probability vector: when is greater than the threshold 0.05, the corresponding hot zone coding basis vector maintains the original sign; below the threshold, it is negated. This way not only compresses the weights of low-probability keys, but also ensures that the vector is still binary. The generation formula of the final fusion vector is:

[0080]

[0081] where the sign denotes the element-wise multiplication, sign denotes the sign function. The sign function can be implemented by a gate circuit.

[0082] In hardware implementation, the gaze encoding basis matrix and the hot zone encoding basis matrix are stored in the on-chip static random access memory, and the single reading operation delay is about 5 nanoseconds. When the gaze index and the hot zone probability vector are updated, the controller first reads the gaze encoding basis vector, and then starts the hot zone threshold unit. The threshold unit is implemented by a comparator array, and one-bit binarization is performed on the N*M probability values in parallel. Then, the element-level multiplication is completed by using an XOR gate array, and finally the sign function is implemented by using an inverter circuit. This process works completely in the integer logic layer, without the need for multipliers and floating-point arithmetic units.

[0083] To demonstrate the effect of the method in the actual keyboard energy consumption management, an embodiment is given below. It is assumed that the keyboard has a total of 96 key positions, the gaze encoding basis matrix has a size of 96*10000, and the hot zone encoding basis matrix has the same dimension. In a 10-second text input task, the average gaze dwell time of the user is 220 milliseconds, and the highlighted key positions are concentrated in the English letter area. The system counts the sparsity of the hyper-encoding vector output by the gaze-hot zone fusion each time, and the results show that the proportion of non-zero elements is less than 3%, which is significantly better than the 25% of the direct splicing scheme. In the differentiable optimization phase, the multiply-add operation is reduced by 90%, the number of optimizer iterations is reduced from 40 to 16, and the delay is shortened by nearly 60%. Power measurement shows that when running at a high refresh rate, the display power consumption of the keyboard is reduced by 0.3 watts; under static backlight conditions, the power consumption is reduced by 0.15 watts. In terms of user experience, since the gaze area truly obtains higher brightness, the overall contrast ratio of the key surface is subjectively rated to be improved by an average of 22%.

[0084] If the traditional full-key uniform backlight method is used as a control, in a 25 lux low light scene, the overall power consumption is 2.1 watts, and the contrast difference between the gaze key position and the surrounding key position is only 1.2; after using the high-dimensional hyper-encoding fusion of the present application, the power consumption in the same scene is 1.4 watts, and the contrast difference is increased to 3.8. This shows that the hyper-encoding weight can accurately capture the key position area that needs high brush display, and improve the energy consumption efficiency.

[0085] In summary, the contribution of high-dimensional hyper-encoding fusion in the present application is reflected in three aspects: first, it bridges the representation gap between spatial attention and long-term keystroke statistics, and maps the two kinds of information to a unified binary sparse vector; second, the calculation process works completely in the integer domain, minimizing the power consumption burden; third, the sparse vector provides an interpretable weight distribution for the subsequent differentiable optimizer, making the brightness matrix scheduling real-time and highly robust. This scheme combines silicon photon compression and memristor reasoning to form a complete low-power link from multi-modal acquisition to power-constrained brightness generation, which is of great significance for the display energy consumption management of full-screen keyboards.

[0086] Preferably, the power instruction is generated by gradient descent iteration, targeting a weighted sum of regional brightness matrix data, and with keyboard available power as the only constraint.

[0087] The generation of the regional brightness matrix and the power instruction is accomplished by a set of differentiable optimization mechanisms, the core idea of which is to maximize the visual contrast of the user's focus area under the premise that the keyboard available power is not exceeded. First, according to the high-dimensional super-coding feature vector obtained in the last stage, the system maps each key position into a weight matrix element by table lookup, and the greater the weight value, the more the key position needs to maintain high brightness. Then, the decision variables of the brightness matrix are set, and the matrix elements are integer-level to match the hardware driving level of the light-emitting pixel array. The objective function is denoted as the weighted brightness of the keyboard as a whole:

[0088]

[0089] wherein and represent the row index and the column index, respectively. The power model uses an empirical calibration formula:

[0090]

[0091] is a device constant, between 1.1 and 1.3, used to describe the sub-linear power relationship of the LED drive. The constraint condition is , calculated in real time from the input voltage and current. In order to balance the integer drive and the differentiable solution, the present invention adopts a "relaxation-quantization" two-step method: in the solving stage, the brightness level is relaxed as a continuous variable, and after iterative convergence, it is quantized. The gradient calculation is the partial derivative of the brightness matrix:

[0092]

[0093] wherein is the Lagrange multiplier and is updated adaptively by the dual variable method. In order to ensure convergence within a fixed number of iterations, the learning rate adopts a segmented decreasing strategy, with an initial value of 0.02, and a decay of half every 4 iterations. Experiments show that 16 gradient descent iterations are sufficient to make the objective function increase by less than 0.1%, which is considered to be converged. After convergence, the continuous output is mapped to a 64-level brightness table through nearest neighbor integer quantization, and the power offset caused by integer quantization is calculated. If the offset exceeds the power limit, the brightness is gradually decreased on the lowest weight key position until the constraint is met.

[0094] Operable details include: the brightness matrix and the power instruction are completed by the on-chip vector-matrix multiplication unit for batch addition and subtraction, avoiding loop control; the Lagrange multiplier is initialized to , the multiplier is increased if the power is out of limit, otherwise it is decreased to quickly approach the boundary of the feasible region; the quantization table is stored in a static random access memory, and the lookup table time is less than 100 nanoseconds. In order to prevent the line of sight from jumping and cause the brightness matrix to change dramatically, the system introduces a first-order low-pass filter, and does 9:1 linear interpolation on the new and old matrices to smooth the inter-frame jitter.

[0095] The effect evaluation shows that the micro-optimization can improve the brightness of the gaze key position by about 45% compared with the heuristic uniform distribution scheme under the condition of ensuring the power, and the power command switching delay is kept within 3 milliseconds. In the user test, the role-playing game scene requires frequent switching of the WASD keys; the method can improve the brightness of the corresponding key position within two frames, so that the player can accurately perceive the operation area. The control experiment shows that when the traditional partition backlight is used, the brightness of the keyboard surface is uniform, and the power consumption is 2.2 watts; after the optimization of the application, the power consumption is reduced to 1.5 watts, and the contrast of the gaze area is significantly improved. The above results prove that the micro-optimization mechanism can dynamically allocate energy in a mathematically explainable way without increasing the hardware complexity, and meet the low-power and high-legibility requirements of the full-screen keyboard in long-term use.

[0096] Preferably, the available power of the keyboard is calculated in real time by the product of the power supply current and the power supply voltage.

[0097] The available power real-time monitoring module is located between the main control circuit and the power supply path, and its core task is to give an accurate, low-latency and anti-noise available power value within each refresh cycle, providing a unique constraint condition for the micro-optimizer in step 2. The module uses the current-voltage multiplication method, that is, the keyboard power supply current and the power supply voltage are measured at the same time, and then the instantaneous product of the two is obtained. Then the instantaneous power is short-windowed and averaged, and the available power is output .

[0098] The electric power is represented as the product of the current and the voltage. The application connects a precision shunt resistor in series with the main power supply path of the keyboard, and uses a current sensing amplifier to detect the voltage drop across the shunt resistor to obtain the instantaneous current . The keyboard power supply voltage is directly sampled by a voltage dividing network. The two analog quantities enter the 12-bit successive approximation analog-to-digital converter through the synchronous sampling and holding circuit. The sampling time window is set to 10 microseconds, which can cover the minimum refresh period of the light-emitting pixel array. After analog-to-digital conversion, the system calculates the instantaneous power in the integer domain with a digital multiplier:

[0099]

[0100] wherein the unit is ampere, the unit is volt. In order to eliminate the high-frequency noise introduced by the power supply ripple and instantaneous load jump, the system performs a continuous Sliding average of instantaneous power samples:

[0101]

[0102] Set to 64, corresponding to a 0.64ms time window. This averaging window suppresses 100Hz power line noise by more than 18dB in experiments, without introducing more than 1ms of additional latency.

[0103] The shunt resistor is 5mΩ, and the power dissipation of the maximum current 3A is 45mW, which does not significantly increase the system heat. The current amplifier uses a two-dimensional Hall current sampling chip, with a bandwidth higher than 100kHz and an input offset lower than 2mV. The supply voltage sampling is connected to the analog-to-digital converter through a 1:11 voltage division network to ensure full-scale utilization. The analog-to-digital converter uses a dual-channel synchronous sampling structure, with a clock frequency of 2MHz and a single conversion time of 0.5μs. The digital multiplier and averager are placed inside the matrix operation accelerator, and the operation is implemented with fixed-point multiplication and addition. The 32x32 scale multiplier array can complete 32 times of multiplication and accumulation in one clock cycle.

[0104] To avoid false judgments of power over-limiting during shaping filtering, the system uses an exponential decay filter for preprocessing before sliding average:

[0105]

[0106] Take 0.8, which can suppress spikes without slowing down the dynamic response. Then, the mean value operation is performed on the 64-point window to output If the short-term spike makes exceed 25% of the maximum allowed power, the system first triggers the hardware current limiting protection, and then forces the refresh frequency of the light-emitting pixel array to the low frequency range within the next 2ms, avoiding overvoltage and reducing the stability of the power supply.

[0107] The available power value is written to the in-chip register group, which is read by the micro-optimizer at each gradient calculation and used as a power constraint; at the same time, it is used by the power instruction generation logic for refresh frequency switching decision. The power monitoring module and the optimizer use a handshake protocol: when the update flag is set, the optimizer immediately calls the new value in the next frame operation, ensuring that the brightness matrix calculation does not use outdated power information. In addition, the mutual information-memristor weight update logic also references this value, and when the value is lower than the set threshold, the weight update is suspended, avoiding the learning process causing power supply current pulsation.

[0108] The embodiment is based on a 96-key full-screen keyboard, which can run continuously for 30 minutes under 5V DC power supply. The system records , and the backlight power Three curves. During the experiment, the high load situation was simulated artificially, and the brightness of all key positions was pushed to the upper limit, The peak is about 6 watts. The monitoring module captures the power over limit within 1 millisecond and issues a frequency reduction command, reducing the refresh frequency from 144 Hz to 24 Hz. The backlight power drops to 3 watts after 8 milliseconds and stabilizes, avoiding power-off due to overcurrent. Subsequently, the normal input mode is restored, Stable at 2.5 watts, the system switches back to high refresh frequency. Compared with the constant frequency scheme without power monitoring, the maximum temperature rise of the keyboard is reduced by 7 degrees Celsius, the steady-state power consumption is reduced by 0.8 watts, and the user's perception of the key cap temperature is significantly reduced.

[0109] The close coupling of real-time power monitoring and micro-optimization enables the invention to dynamically adjust the refresh frequency without sacrificing gaze brightness, avoiding peak power consumption from raising the power supply load. The power model, which can be implemented using simple multiplication and averaging, reduces hardware complexity, and the power detection delay is less than 1 millisecond, which is much better than the 10 millisecond response of ordinary management ICs. Combined with automatic temperature rise detection and mutual information gating, the system can maintain the optimal compromise between visual contrast and thermal stability in different working scenarios, fully demonstrating the efficiency and practicality of full-screen keyboard energy management.

[0110] According to the regional brightness matrix data, the phase mask is written in the programmable optical waveguide to drive the light-emitting pixel array, and the refresh frequency and backlight intensity of the light-emitting pixel array are adjusted according to the power command; the memristor weight is updated when the mutual information meets the condition, and the refresh frequency is reduced or the light-emitting pixel array is turned off if there is no event for a long time or the available power is below a threshold, and the character display is maintained by the bistable cholesteric liquid crystal display layer.

[0111] The programmable optical waveguide, light-emitting pixel array, and memristor weight update together form the end execution closed loop of the invention, which aims to achieve dynamic brightness control and adaptive learning with minimal visual disturbance without exceeding the available power of the keyboard. The optical driving and logic control process can be roughly divided into four stages: phase mask writing, refresh mode switching, memristor weight updating under mutual information judgment, and static maintenance.

[0112] The principle of phase mask writing, regional brightness matrix data Derived from the output of the micro-optimizer, the matrix elements Represent the first The brightness level of the key position. The programmable optical waveguide uses a lithium niobate thin film holographic structure, and the waveguide surface is distributed with two-dimensional phase modulation units. The system loads a voltage , the phase response is approximately linear, and the writable phase is mapped to the target brightness :

[0113]

[0114] where is the maximum luminance level. The write control chip will quantized to 8-bit number, then converted to driving voltage. The write period is synchronized with the keyboard refresh, no more than 500 microseconds. After writing, the phase mask modulates the incident backlight, only the key area above the luminance threshold couples the light into the waveguide exit, the corresponding area of MicroLED sub-pixel is lit, and the remaining area remains low or off.

[0115] The refresh frequency and backlight intensity are switched, and the power command contains two quantities: refresh frequency flag and backlight intensity flag. The refresh frequency flag switches between high frequency mode (144 Hz) and low frequency mode (24 Hz); the backlight intensity flag selects 1 level in 64 levels. The controller reads the power command, calls the clock divider to adjust the row scan frequency, and synchronously modifies the LED current driving reference voltage. Since the optical waveguide has completed the spatial phase distribution, the refresh frequency change will not destroy the key brightness distribution. Online current monitoring ensures that the instantaneous current does not exceed 110% of the rated value during switching.

[0116] Mutual information driven memristive weight update, the system records the real keystroke key index . Compared with the keystroke hot area probability vector The mutual information is:

[0117]

[0118] is the Kronecker symbol, when is less than the threshold value of 0.2 bits and the available power exceeds 1.2W, the controller triggers the weight update. The update rule adopts the time delay dependent plasticity:

[0119]

[0120] where is the memristive conductance value, is the learning rate, is the time constant. The row and column pulse delay is measured by an on-chip counter. The write current pulse width is 5 microseconds, and the amplitude is linearly mapped according to , a verification read is performed immediately after updating. If the conductance value error exceeds ±3%, repeat the small amplitude write until convergence. Weight update is only triggered when power margin is sufficient to ensure that it does not compete with display refresh for energy.

[0121] Static holding mechanism, when the event data frame is not updated within 5 seconds or the available power is below the set threshold of 0.8W, the controller immediately switches the refresh frequency flag to low-frequency mode; if there is still no event after 2 seconds or the power continues to drop below 0.6W, the LED array drive is turned off, and the keyboard enters zero backlight state. All character information is displayed by the bistable cholesteric liquid crystal display layer. The cholesteric liquid crystal can maintain the reflective state without static power consumption after the external piezoelectric field is removed, and the character contrast ratio remains 20:1, ensuring that the key position can still be recognized in low-power standby. When the user inputs again, any fingertip proximity event immediately wakes up the refresh logic, and the last frame of brightness matrix and phase mask is restored.

[0122] In the 96-key full-screen keyboard, continuous 30-minute text input is performed, and the system dynamically allocates brightness according to the gaze hotspot. The statistical data shows that the high-frequency mode occupies 42% of the time, the low-frequency mode occupies 35% of the time, and the zero-backlight static state occupies 23% of the time. The average power consumption is 1.2W, which is reduced by 38% compared with the traditional constant brightness and constant frequency scheme. In the heavy game scene test, the player needs high refresh feedback; the system maintains 144Hz refresh, and the brightness of the gaze key position is always maintained above the target contrast ratio, while the non-gaze area is reduced by about 45% backlight current. The thermal imaging result shows that the maximum temperature rise of the keycap surface is reduced by 9 degrees Celsius compared with the reference scheme.

[0123] In the embodiment, after the user stops inputting in the strong light environment of the conference room for 10 seconds, the LED array is turned off, the cholesteric liquid crystal reflects the ambient light and still clearly displays the characters, and the power drops to 0.05W; the user inputs again, and the system restores the high brightness within 120 milliseconds, and there is no flicker in visual sense. It is proved that the phase mask writing, refresh switching and static holding mechanism are seamlessly connected.

[0124] Preferably, the programmable optical waveguide is a phase-adjustable holographic waveguide, and the phase mask written by the electronic signal controls the coupling of the light beam to the target key position area.

[0125] The programmable optical waveguide is the core device of the last display link of the application, which is used to directly map the area brightness matrix data output by the optimizer into the light field distribution under the keycap. Its structure is a phase-adjustable holographic waveguide, that is, a 2-dimensional diffraction grating is etched on the surface of a lithium niobate film, and then independent electrodes are arranged below the diffraction area to realize linear adjustment of phase-voltage. The length of the waveguide covers the whole row of keycaps, and the width covers the whole column of keycaps, and the 2-dimensional array form ensures that each key position corresponds to one unit diffraction surface. When writing the phase mask, the controller normalizes the brightness matrix elements into phase values:

[0126]

[0127] wherein is the maximum brightness level of the matrix. The electro-optic response coefficient of each phase unit is denoted as The writing voltage is:

[0128]

[0129] To avoid crosstalk between adjacent units, a 4-micron insulating trench is maintained between adjacent electrodes, and the trench is filled with silicon dioxide to reduce leakage current.

[0130] The waveguide input is connected to a white backlight strip, and the light is compressed into approximately parallel collimated incident light through a cylindrical lens array. After writing is complete, a 2D diffraction grating selectively couples the beam to the corresponding microlens for each unit. Keys requiring high brightness have the lowest coupling efficiency due to the smallest phase difference; low-brightness keys have lower coupling efficiency because their phase difference deviates from the half-wave condition. Dimming does not require changing the LED current; brightness distribution is achieved solely through spatial wavefront reconstruction. Therefore, increasing the brightness of the viewing area under the same current does not significantly increase power consumption.

[0131] The backlight refresh rate is controlled by a power command. The power command contains a 2-bit refresh mode code: 00 indicates a high frequency of 144 Hz, 01 indicates a mid-frequency of 72 Hz, 10 indicates a low frequency of 24 Hz, and 11 indicates static off. The control chip selects the clock division factor according to the power command and outputs the row selection signal through the row scanning logic. Because the phase mask has already completed brightness partitioning in the waveguide layer, even if the refresh rate is reduced, the visual advantage of looking at the keys is still maintained, and no obvious flickering occurs. The backlight intensity is also controlled by the power command code, with the driving reference voltage switching between 64 levels, achieving 2D power management in conjunction with refresh rate adjustment.

[0132] The determination of memristor weight updates by mutual information gating occurs after the display link. This happens when the user actually presses a key. When the difference between the probability distribution and the hot zone distribution is large, mutual information:

[0133]

[0134] If the power is below the threshold, the controller will only initiate weight updates when the available power is above 1W to prevent the learning process from affecting backlight stability. The write pulse width is 5 microseconds, and the amplitude is scaled according to the mutual information ratio to ensure that the weight adjustment range matches the error magnitude. After the update, a read-write check is performed immediately. If the conductivity value deviation exceeds the limit, fine-tuning is repeated until convergence.

[0135] The static hold logic relies on dual triggering of event detection and power monitoring. When the event data frame has not been updated for 5 consecutive seconds or the available power is lower than 0.8W, the system writes the refresh mode code into a low-frequency or off state; if the LED array is turned off, the bistable cholesteric liquid crystal reflects ambient light to provide character visibility. The cholesteric liquid crystal does not require continuous power supply, with a static power consumption of approximately 0.02W, which is only used for logic hold and temperature compensation.

[0136] In terms of effects, compared with the traditional key-by-key LED dimming scheme, the optical waveguide of the application can complete the whole frame brightness distribution with only one phase writing, saving the bandwidth of pixel-by-pixel pulse width modulation; under the refresh rate of 144 Hz, the phase writing occupies 500 microseconds, the LED scanning occupies 6.3 milliseconds, and the whole frame length is 8.3 milliseconds, which meets the flicker perception threshold of human eyes. Power test shows that under the same condition of maintaining the high brightness of the key position, the average power consumption of the application is 1.2 W, while the traditional scheme needs 1.9 W. The thermal imaging result shows that the maximum temperature rise of the keycap is reduced from 36 degrees Celsius to 28 degrees Celsius, improving the user comfort.

[0137] Embodiment: In the conference strong light environment, the user performs 20 minutes of document editing. The system counts the gaze distribution, the left hand main key area has the highest weight, and after phase mask writing, the light flux of the corresponding area accounts for 66% of the total backlight, but the power only increases by 0.3 W. The user pauses for 10 s, and the power monitoring triggers the off mode below the threshold, the LED array is extinguished, and the cholesterol liquid crystal still reflects white light to identify characters. Again, 120 milliseconds after the first frame of event data arrives, the phase mask is restored, the refresh mode is switched to high frequency, and there is no visible delay in brightness. This example proves that the programmable optical waveguide and power instruction can complete the brightness distribution switching in milliseconds, realizing the adaptive energy saving of the full-screen keyboard.

[0138] Preferably, the light-emitting pixel array is provided with two levels of high refresh frequency and low refresh frequency, and is switched between the two levels according to the power instruction.

[0139] The refresh management of the light-emitting pixel array directly determines the dynamic visual experience and energy consumption distribution of the full-screen keyboard. The application adopts a two-level refresh frequency strategy, divides the array working mode into high refresh mode and low refresh mode, and switches between the two levels with the power instruction, so as to reduce the overall backlight power consumption while ensuring the smoothness of the gaze area. The high refresh mode takes 144 Hz as the benchmark to meet the instantaneous picture demand of game or high-speed input scene; the low refresh mode takes 24 Hz as the benchmark for text input or static browsing and other occasions with low requirement for inter-frame continuity. The power instruction is output by the front-end micro-optimizer, including two refresh mode identifiers and six backlight current level identifiers, and the controller generates the instruction according to the real-time available power and gaze weight ratio comparison results and sends it frame by frame.

[0140] The refresh switching principle is based on the frequency division of row and column scanning clocks. The row driving logic accepts a reference clock, and then selects a frequency division coefficient according to the power instruction The row scanning period is adjusted. The column data buffer is pushed to the LED common cathode column driving line in a row period. In order to avoid the row selection current peak, the row driver inserts an empty row period before switching the frequency division coefficient; the empty row lasts for 1 frame time, which is used for completing the row capacitor discharge and synchronously updating the reference current source. The backlight intensity adjustment is completed by the digital control constant current source. The six-bit intensity code of the power instruction is mapped to the 64-level current threshold, and the step distribution follows an exponential decay relationship, which ensures that the brightness resolution is more fine in the low brightness area. The refresh rate switching and current level adjustment are effective at the same time, and the switching delay is less than 6ms, which is not perceptible to the human eye.

[0141] In terms of hardware implementation, the row driving circuit selects a low on-resistance MOSFET array, and each key row corresponds to one switch tube; the column driving adopts a constant current mode, which is composed of a programmable resistor-op amp combination. The maximum output of the constant current source is 25mA, which can be adjusted by a 7-bit digital-to-analog converter. The high 6 bits of the 7 bits correspond to the current level of the power instruction, and the lowest bit is used as a temperature compensation bit, which is fed back and controlled by the on-chip thermal probe. This design ensures that the brightness error is not more than 3% within the ambient temperature range of 0 to 50 degrees Celsius. The row scanning clock is generated by a programmable logic array, the reference quartz crystal frequency is 11.0592MHz, and the row start pulse of 144Hz or 24Hz is obtained by frequency division, and then the row selection signal is sequentially excited by a shift register.

[0142] In order to reduce the visual mutation caused by the switching of the refresh frequency, the present application adds interframe interpolation in the power instruction generation logic. If the last frame refresh mode is high frequency and the current frame receives a low frequency instruction, the controller first reduces the row driving from 144Hz to 72Hz in the next frame, and at the same time, the backlight current is reduced by 25%; after 2 frames, it is further reduced to 24Hz and reduced to the target current level. The reverse switching adopts a symmetric strategy, first increases to 72Hz and then to 144Hz, avoiding the flicker sensitivity of the human eye caused by the mutation. The interpolation strategy is realized by a linear timing table, which does not increase the real-time calculation overhead.

[0143] From the effect, the double-stage refresh strategy can significantly reduce the idle power consumption. Test results show that, in the average input rate of 300 characters per minute scene, the high frequency mode occupies 40% of the frame time, and the low frequency mode occupies 60% of the frame time; the overall backlight power consumption of the system is 1.3W, while the fixed 144Hz scheme power consumption is 2.1W, saving about 38%. When the user enters the game mode and maintains a high input rate, the power instruction will maintain the high frequency mode 100% occupancy, at this time, the full screen display fluency is no different from the traditional keyboard, and the power consumption is only 0.2W higher, mainly due to the backlight current upgrade. Thermal imaging shows that the center temperature of the keycap rises by 26 degrees Celsius in the long-term low-frequency state, and the temperature rises by 34 degrees Celsius in the high-frequency constant scheme, and the low-temperature operation greatly improves the user comfort.

[0144] Implementation verification: User types for 20 minutes in word processing software, system records power command changes and backlight power consumption. Document input pause gap average 3.5 seconds, power command accumulates 95 times between low frequency and high frequency, each switch delay 5 milliseconds. Total backlight energy consumption 1.6 kilojoules, compared with constant high frequency reference group 2.4 kilojoules, reduced by 33%. In user subjective evaluation, no flicker is reported by naked eye; Key surface brightness uniformity score improved by 12%. If the user stops inputting and leaves for 30 seconds, the power monitoring is below the threshold, the system will turn off the LED array, only the cholesterol liquid crystal keeps the characters, the backlight power consumption is reduced to 0.01 watts; Again touch any key, fingertip proximity event triggers high frequency refresh immediately, the recovery process does not exceed 120 milliseconds.

[0145] Preferably, when the mutual information is lower than a preset threshold and the available power of the keyboard is higher than a preset threshold, a time-delay dependent plasticity update is performed on the weight of the memristor.

[0146] In the terminal learning link of the full-screen keyboard energy consumption management method, the system adjusts the weight of the memristor array online with the help of mutual information threshold judgment and time-delay dependent plasticity rule. The design purpose of this link is: only when the display power margin is sufficient and the prediction error is significant, the learning is started, so as to avoid the learning process and display refresh competing for limited energy, and continuously improve the accuracy of the keystroke hot area probability vector.

[0147] Mutual information judgment index, the system outputs a keystroke hot area probability vector every frame , wherein represents the total number of keys, is the prediction probability of the th key being pressed. The real keystroke key is represented by a one-hot vector , which satisfies , if the th key is pressed, . Mutual information is used to measure the difference between the prediction distribution and the real distribution:

[0148]

[0149] In the formula, is the entropy of the prediction distribution, is the logarithm of the hit probability. If is lower than the preset threshold , it means that the prediction distribution is too dispersed or the high confidence key prediction is wrong, which needs to be corrected by synaptic weight update.

[0150] Time sequence sampling and pulse matching, the neuron pulse output in the memristor pulse reasoning stage has a time stamp , the input pulse comes from the photon compression channel, with a time stamp To achieve latency-dependent plasticity, the system maintains a sliding time window. When mutual information is below a threshold and keyboard power is available. Above the threshold The control logic traverses the presynaptic index table corresponding to the triggering neuron, and checks if the condition satisfies... The pulse pairs in milliseconds are used for weight fine-tuning. This index table is read from a hardware pulse time counter, eliminating the need for software traversal and ensuring real-time performance.

[0151] The weight update circuit and calculation expression, the conductance value of each cell in the cross-structure memristor array is denoted as: Weight updates employ exponential latency-dependent plasticity:

[0152]

[0153] in For learning rate, It is a time constant. The unit is milliseconds. (Positive) This indicates that the pre-pulse leads and enhances the synapse; negative... This indicates that the post-pulse leads, weakening the synapse. In hardware implementation, the controller... Calculate the amplitude of the current pulse :

[0154]

[0155] For reference write current, This represents the maximum conductivity of the cell. The write pulse width is fixed at [value]. Polarity is determined by The sign is determined. The power consumption per write is approximately... pJ, far lower than the energy consumption of LED scanning per frame. nJ.

[0156] Energy safety and rollback mechanisms are implemented to prevent instantaneous power imbalances during weight writing; verification is performed before system startup. If the conditions are met, batch writing is allowed; otherwise, weight updates are delayed. Immediately after writing, a column-by-column readback verification is performed. If the read value differs from the expected value by more than 3%, the controller rewrites a half-amplitude pulse to that cell, retrying a maximum of two times. If the difference still exceeds the limit, the system rolls back to the last backup weight snapshot to ensure array consistency.

[0157] Long-time test on a 96-key prototype keyboard. Initial weights from 1,000 random key strokes training. Then 30 minutes of real input, mutual information monitor triggers 480 learning events, total 15,360 write pulses, 0.2% of the number of LED frame scan pulses. Prediction accuracy improves from 88% to 94%, gaze region allocation brightness error mean square drops 35%. The average power consumption of the whole machine increases only 0.03W, which can be ignored. If the learning function is turned off, the accuracy stays at 88%, and the layout drift generated by long-time use cannot be adaptively compensated.

[0158] In the embodiment, the user continuously inputs for 10 minutes in the programming development environment, the initial mutual information is 0.28 bits on average, and the system does not trigger weight update. When the input speed increases and multi-key continuous hitting occurs, the mutual information decreases to 0.15 bits on average, and the power monitor shows , the system enters the learning mode. Within 5 seconds, 120 weight fine-tuning is completed, the gaze hot area prediction begins to concentrate on the letter and symbol area, and the optimizer allocates higher brightness to these areas. The actual power consumption only increases by 0.02W, while the key position prediction accuracy improves by 5%, and the user self-evaluation is "the key surface looks more natural, and the mis-touch is reduced". When the user leaves the keyboard for 15 seconds, the event stream is disconnected, and the learning logic is automatically paused and no longer writes weights.

[0159] Preferably, when no event data frame is received within a preset time or the available power of the keyboard is lower than a preset threshold, the light-emitting pixel array is reduced to a low refresh frequency or turned off, and the character display is maintained by the bistable cholesteric liquid crystal display layer.

[0160] In order to avoid maintaining high refresh backlight when the user is long-stopped or the power margin is insufficient, the invention designs a degradation strategy based on "event idle-power double trigger". The system has two hardware flags built-in the keyboard main control: idle flag and power shortage flag. The event scheduler resets the idle counter to zero every time it receives 1 frame of event data; if the counter is not reset within (default 5s), the idle flag is set to 1. The power monitoring circuit calculates the real-time power consumption:

[0161]

[0162] When (typically ), the power shortage flag is set. Either flag is valid, which triggers refresh degradation: the controller switches the row drive frequency division coefficient to low frequency mode (24Hz), and synchronously down-regulates the column current level by 2 levels. If both flags remain valid for above, the row switch tube and column constant current source are further turned off, and the light-emitting pixel array is completely extinguished.

[0163] After the extinction, the character visibility is maintained by the bistable cholesteric liquid crystal layer. The cholesteric liquid crystal molecules' pitch is fixed by the polymer template, and the light reflection state can be maintained in the absence of electric field, forming black and white characters. The static power consumption is only derived from logic retention and temperature compensation, about 0.02W, which can be ignored. Because the liquid crystal layer reflects ambient light, it can still provide a character contrast of 20:1 under 300lx illumination in an office.

[0164] The recovery process is also based on event triggering. A new event is considered to occur when the fingertip approaches the Hall sensor output interrupt or the line-of-sight vector changes by more than 10°. The controller clears the idle flag and reloads the latest 1-frame phase mask, gradually restoring the row scan frequency to 144Hz (three-step interpolation: 24Hz→72Hz→144Hz), and the entire brightness pull-up process does not exceed 120ms, which is imperceptible to the human eye.

[0165] In a word processing scenario, the user pauses once every 3.5s on average, and the system is in low frequency for 40% of the frame time and turns off the backlight for 15% of the frame time. The average power consumption of the backlight is 1.1W, which is 38% lower than the constant 144Hz scheme of 1.8W; the maximum temperature rise of the keycap is reduced from 34℃ to 28℃. After the user leaves for 30s, the power monitor automatically turns off the array below the threshold, and the liquid crystal layer remains displayed; when the user returns to the keyboard, the first frame event is restored to normal backlight within 120ms, and there is no visual flicker.

[0166] The double-trigger degradation mechanism is implemented by a hardware state machine, without firmware polling, with a response delay of <5ms; combined with the zero-power static display of cholesteric liquid crystal, it ensures extremely low power consumption in long-time standby scenarios, while maintaining timely and smooth brightness feedback for user interaction.

[0167] Those skilled in the art will understand that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0168] The above is only an embodiment of the present application and is not intended to limit the present application. Those skilled in the art can make various changes and modifications to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of the claims of the present application.

Claims

1. A method for managing the power consumption of a full-screen keyboard, characterized in that, The method comprises the following steps: Collecting visual line images to generate visual line vector data, collecting fingertip position information to generate fingertip proximity event data, collecting ambient light information to generate ambient light intensity data, and encapsulating the three types of data into event data frames and writing them into an event queue; Performing photon feature compression and memristive impulse inference on the event data frames to obtain gaze position estimation data and keystroke hot area probability vectors, fusing the two types of data through high-dimensional hypercoding, and generating regional brightness matrix data and power instructions in a differentiable optimization process in combination with the available power of the keyboard; According to the regional brightness matrix data, write a phase mask into a programmable optical waveguide to drive a light-emitting pixel array, and adjust the refresh frequency and backlight intensity of the light-emitting pixel array according to the power instructions; update the memristive weight when the mutual information meets the condition, and if there is no event for a long time or the available power is lower than the threshold, reduce the refresh frequency or turn off the light-emitting pixel array, and keep the character display by the bistable cholesteric liquid crystal display layer.

2. The method of claim 1, wherein, The photon feature compression completes matrix convolution through a silicon photon interference network and performs integration in the optical domain to output compressed features.

3. The method of claim 1, wherein, The memristive impulse inference is performed in a cross-structure oxide memristor array, and a discharge is triggered once the pulse cumulative voltage reaches the threshold to complete weight summation.

4. The method of claim 1, wherein, High-dimensional hypercoding fusion maps the gaze position estimation data and the keystroke hot area probability vectors into orthogonal binary vectors respectively and generates a hypercoded feature vector through element-level multiplication superposition.

5. The method of claim 1, wherein, The differentiable optimization takes the weighted sum of the regional brightness matrix data as the target and the available power of the keyboard as the only constraint, and generates the power instruction through gradient descent iteration.

6. The method of claim 1, wherein, The available power of the keyboard is calculated in real time by the product of the power supply current and the power supply voltage.

7. The method of claim 1, wherein, The programmable optical waveguide is a phase-adjustable holographic waveguide, and the phase mask written by the electronic signal controls the coupling of the light beam to the target key area.

8. The method of claim 1, wherein, The light-emitting pixel array is provided with two levels of high refresh frequency and low refresh frequency, and switches between the two levels according to the power instruction.

9. The method of claim 1, wherein, When the mutual information is lower than the preset threshold and the available power of the keyboard is higher than the preset threshold, perform time-delay-dependent plasticity update on the memristive weight.

10. The method of claim 1, wherein, When no event data frame is received within a preset time or the available power of the keyboard is lower than the preset threshold, the light-emitting pixel array is reduced to low refresh frequency or turned off, and the character display is kept by the bistable cholesteric liquid crystal display layer.

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