Digital Data Processing Details TRIZ Cases

A hybrid neural network and Volterra path cuts DPD power and complexity while compensating power amplifier nonlinearity.

Reusing adder circuitry lets a 4-LUT expose intermediate mux outputs, enabling smaller LUT functions with less delay and better area use.

Handles out-of-order Reed-Solomon symbols with multiplexor-based syndrome calculation that cuts hardware overhead and latency.

A high-transmittance screen region above the transmitter reduces signal attenuation and enables longer-range object detection in full-screen devices.

An FPGA masking circuit applies or generates masks on-chip to cut memory bandwidth, storage overhead, and energy in transformer workloads.

Adaptive matrix updates track data distribution drift to keep compression efficiency and cryptographic security aligned across changing streams.

A simplified superset FPU converts mixed FP8 inputs into a unified FP9 format to cut decoding power, delay, and area in accelerators.

Dynamic model monitoring retrains encoding and decoding codebooks to compact already compressed data while improving storage density and transmission efficiency.

Configurable synchronized load modules emulate CPU and GPU current profiles, helping verify PMIC voltage regulation under dynamic SoC loads.

Dynamic hardware management adjusts memory, processors, and FPGA usage to keep encrypted BWT-based compression efficient under changing loads.

Capacitive proximity sensors detect phone-to-user position so dead reckoning runs only when reliable, improving indoor tracking and battery life.

Current-grade classification and tailored ADC conversion improve analog MAC digitization accuracy while keeping neural computing energy low.

A magnet-guided function key coordinates with a sliding flexible display to expand screen area while keeping the electronic device compact.

Memristive control of transistor gate charging enables variable threshold operation and efficient hardware calculations with lower leakage currents.

Closed-loop back-gate control matches two FD-SOI ring oscillators, cutting quantization noise and power in coherent-sampling TRNGs.

Time-division multiplexing and vector quantization simplify unary-coded MAC accumulation, cutting circuit complexity and power use.

Fountain-code data blocks and synthesized polynucleotide strands improve DNA storage reliability while supporting error recovery.

Directly converts higher-precision mantissas to lower precision during rounding normalization while detecting carry and overflow without integer conversion.

Dynamic bit allocation in EPosit and IPosit improves precision and dynamic range while reducing hardware and bandwidth demands.

A board management controller switches voltage regulators and load paths by workload profile to cut wasted power in programmable logic cards.

Different voltage islands in programmable logic cut power use while limiting voltage drops and noise on the power distribution network.

Placing a circuit component between emitter and detector blocks direct radiation, cutting measurement noise without a separate shield.

Stochastic rounding and prefix-coded exponent sizing reduce bias, error accumulation, and hardware overhead in mixed-precision conversion.

Crossbar-based in-memory VMM cuts data transfer time and power by combining neural computation and weight storage in one circuit.

Anchored HPA number conversion preserves associative sums, enabling deterministic SIMD parallel arithmetic and easier debugging.

A dual-processor playback architecture shifts basic audio tasks to a low-power core, waking the GPOS processor only for complex features.

Precomputed exponent and mantissa lookup values compress floating-point RDM data into dB form for faster, lower-bandwidth telemetry transmission.

Mask-guided vector decompression restores sparse neural network weights with fewer instructions, cutting memory bandwidth, cycles, and power.

Controlled capacitor charging and threshold detection improve analog multiply-accumulate accuracy while keeping neural-network power use low.

A resistor-string DAC with main and sub converters replaces HVMOS and level shifters, shrinking display driver IC area.

Using radix-4 Booth encoding and differential RRAM weight storage, this MAC circuit cuts data movement and power for parallel neural computing.

Separating sign, exponent, and mantissa data enables model compression while limiting quantization error and bandwidth use.

A mesh transmission line and counter-based delay checks detect microprobing and FIB edits while reducing semiconductor security overhead.

Parallel single-bit polyphase FIR paths filter unary-coded data directly, avoiding high-rate conversion while cutting complexity and power.

Parallel core processes shorten the critical path in recursive sinusoid synthesis, while refresh updates limit quantization error.

An ECC logic circuit and MAC operator work inside memory to cut data transfer bottlenecks and speed neural network computation.

Sparse signal samples are split into binary words so zero words can be skipped, cutting MAC operations and energy use without losing precision.

CRC logic flags read/write failures inside a PIM MAC path, cutting memory-processor transfers while preserving AI compute accuracy.

Keep logic and multiplexor arrays drop low-significance matrix products to cut MAC workload, execution time, and energy with acceptable accuracy.

Separate PAM and NRZ signal paths raise memory data rates while preserving reliability and limiting power growth.

Alternating carry inversion and staged clock delays cut ripple-adder propagation delay while keeping logic simpler than carry look-ahead.

Idle-state supply shifting and transistor body biasing cut IoT circuit leakage while preserving active performance and fast wake-up.

A low-power SPOS processor runs basic playback and wakes a GPOS co-processor only for complex audio and voice tasks, preserving battery life.

Mask bits mark zero weights so only non-zero kernel values are stored, cutting neural network memory traffic and buffer usage.

Current summation inside 3D AND flash memory cuts data movement and circuit area for faster multiply-accumulate computing.

Pre-verified embedded arithmetic blocks replace larger DSP-style logic in structured ASICs, cutting die area and cost while keeping arithmetic flexibility.

Individually addressable actuators and a biasing layer deform flexible display regions to add haptic texture while keeping the structure thin.

Bit-type interleaving and selective compression cut DNN model transmission load while preserving prediction accuracy during external transfer.

Block-scaled filter weights are converted for MAC image pipelines to widen dynamic range without increasing memory allocation.

State-dependent charging speed and threshold detection improve analog multiply-accumulate resolution and accuracy with low power use.