Digital Data Processing Details TRIZ Cases

Dedicated activation, weight, and output buffers avoid memory conflicts and cut power while matching memory layout to each neural network.

A shared EMR and capacitive sensor layout detects both stylus pens and fingers while cutting touch-controller channels, thickness, and cost.

An overflow-controlled analog accumulation stage lets CiM arrays digitize less often, cutting ADC energy per MAC without losing precision.

Separating floating-point data into compressible and uncompressible parts cuts model size while limiting information loss in transmission and storage.

Capacitive spacing between the display panel and image sensor enables accurate fingerprint capture while preventing flex contact damage.

Asynchronous unit-element multiplication uses charge transfer and ADC output to cut power dissipation in scalable dot-product hardware.

A decoding tree and bit-position control signal cut SCF polar decoding memory and power while supporting efficient hardware re-decoding.

Zero detection and disable circuits skip unchanged operations in computational memory, cutting data-movement power and enabling result-vector error checks.

Digital calibration nodes correct DAC offset, amplifier distortion, and gain errors in analog ML accelerators to improve neural network accuracy.

Dedicated correction cells cancel goff spurious currents in ANN NVM crossbars, improving MAC accuracy in power-constrained hardware.

A pipelined zero-value and shared-value codec cuts neural network data volume to ease memory bandwidth and power demands.

By reading all possible current states before selection, this case pipelines table lookups and enables parallel input processing at higher clock speeds.

A middle-electrode RRAM structure compares filament resistance states to generate random bits with stronger read-noise margin and retention.

RC charging time replaces per-column ADCs in this MAC circuit, cutting power use while speeding vector-matrix operations in AI hardware.

Separating compressible and uncompressible floating-point data enables smaller ML models with controlled error and lower storage and bandwidth use.

A stacked 3D memory array performs neural multiply-accumulate operations while shortening routing rails to cut power and improve speed.

Two I/O voltage supplies and 3-level PAM raise memory bandwidth while cutting power and supporting flexible LPDDR package configurations.

In-memory MAC computation cuts data transfer overhead by converting 32-bit floats to 16-bit formats and modulating bit width for faster AI processing.

Three programmed crossbar sections detect and correct analog matrix-computation errors in one cycle, cutting latency, power, and lookup-table complexity.

Retention flip-flops preserve peripheral logic states in low-power mode, cutting microcontroller energy use without full reset delays.

Charge-based multiplication units and switched accumulation lines cut MAC power while easing analog circuit integration for neural processing.

A Dot field varies exponent and mantissa bit widths to balance numerical range, precision, and storage overhead in AI and HPC.

Different bit truncation for outlier and non-outlier neural network parameters cuts memory power use while preserving model accuracy.

A resonance-based touch module replaces bulky mechanical switches, enabling sealed, compact input sensing for dustproof and waterproof devices.

Dynamic switching between lossy and lossless compression cuts memory bandwidth demand in sparse matrix arithmetic transfers.

Lower-precision forward and backpropagation with higher-precision weight updates cuts neural network training time while preserving accuracy.

Pre-characterized force maps calibrate virtual buttons, improving press detection accuracy while preserving waterproof, dirt-resistant surfaces.

An inaudible drive signal makes the loudspeaker diaphragm move air for active cooling when compact sound devices overheat in standby.

Using logarithmic-format addition and accumulation cuts floating-point conversion overhead, lowering power use and improving inferencing throughput.

Per-block multiplexor switching selects only the needed voltage rail to reduce wasted memory power while preserving retention across modes and temperature.

Shared exponent identifiers and lazy accumulation cut floating-point overhead while preserving mantissa bits across large exponent differences.

Board-specific voltage offsets let an SoC compensate for IR drop and PMIC variation, cutting power use while protecting yield.

A latch and counter toggle a fixed output after a set interval, helping downstream circuits exit power-down mode and avoid excess power use.

Local WFST acoustic decoding plus software grammar scoring cuts bandwidth, improves endpoint detection, and preserves speech privacy.

Segmented adder nodes compute sums and carries independently, cutting area and latency in very large integer arithmetic circuits.

Hidden touch sensors and a porous cover create a compact smart speaker housing that preserves touch input and clear microphone access.

Offsets the exponent to drive mantissa shifting without bias subtraction, cutting floating-to-fixed conversion time and hardware.

A single-chip drive circuit combines display driving and touch sensing to expand scan functions, improve power control, and shrink circuit area.

Clock-cycle-based ODT control keeps the path active through consecutive pin flips, then disables it to cut current waste and power use.

PWM start-up speed sequences distinguish similar fans, enabling accurate type identification for control tuning, traceability, and energy efficiency.

Separating neural network weights into lossless exponents and LUT-compressed mantissas cuts training latency, area, and power with limited accuracy loss.

Fixed-point weight scaling minimizes quantization error so neural networks use less memory and power without major accuracy loss.

Separate PAM and NRZ signal paths let a memory controller boost read/write speed while lowering power use and improving reliability.

RC charging and a shared TDC replace separate ADC-heavy MAC paths, boosting neural network data throughput with lower power and area.

Per-channel statistics set bias and weight fractional lengths, cutting neural network operations on low-power devices while preserving accuracy.

An exponent-offset shifter converts floating-point data to fixed-point format faster by removing bias subtraction and reducing logic hardware.

Phase-shifted waveform snippets approximate sideband frequencies while cutting memory workload and computation time for waveform generation.

Sequential bit-edge detection converts DWA words to binary at high speed while balancing unary element use to lower mismatch noise.

Localized housing heat dissipation is adjusted from contact and non-contact temperatures to cool 5G phones without causing a burning sensation.

A voltage-detection circuit and CTANK capacitor let a 2-wire sensor bus enter low-power mode during under-voltage without resets.