Computation Using Non-Contact Making Devices TRIZ Cases

Sub-binary radix weights and SRAM multipliers calibrate analog neural computing to curb device mismatch, power use, and output variation.

Precomputed lookup tables replace floating-point multiplication and dot-product math to cut AI compute time with controlled approximation.

A reconfigurable FPGA DSP block uses flexible precision and efficient weight loading to handle AI math and signal processing on shared hardware.

Prime-number multiset encoding cuts storage cost while enabling fast add, remove, and identification of user-device connections.

Removing least significant bits before partial-product summation cuts multiplier area and power while bias compensation limits truncation error.

Grouped multipliers, adders, and function blocks enable massively parallel neural inference with lower latency and faster neuron activation processing.

Direct exponent-based shifting removes bias subtraction in floating-to-fixed point conversion, cutting circuit hardware and conversion time.

Modified Gram-Schmidt loop reordering and ALU-to-ALU FPGA connectivity raise QR decomposition throughput while cutting memory use.

Precomputed lookup tables speed floating-point multiplication and vector dot products with 3D memory, symmetry, and zero-skipping.

Sense circuitry performs AND, OR, and SHIFT operations inside the memory array to cut data transfer, power use, and multiplication time.

Direct exponent-based shifting converts floating-point values to fixed-point format faster while avoiding bias subtraction hardware.

A single DSP slice switches across numerical modes with control signals, reducing extra fabric logic and improving processing speed.

Parallel multipliers, adders, and function blocks overcome sequential neural inference bottlenecks to cut latency and raise throughput.

Truncating least significant partial-product bits cuts multiplier area and power, while bias compensation limits quantization noise in RF converters.

Distance encoding of selected nonzero bits cuts memory and compute for quantized real signals while preserving precision for multiplication and convolution.

A split-path shifter converts floating-point data to fixed-point format without exponent bias subtraction, reducing delay and hardware.

An intermediate significand-exponent-shadow format shifts value windows to keep floating-point sums accurate and independent of operand order.

A stacked memory-and-logic processor uses an in-package LUT to handle complex mathematical functions with higher density and a smaller footprint.

Embedded sensing circuitry performs AND, OR, and shift operations inside the memory array, cutting data-transfer time and power.

By removing least significant bits from partial products and compensating DC bias, this multiplier cuts circuit area and power.

A 20-bit floating-point format uses 4-bit exponent mapping and 14-bit mantissa allocation to cut signal distortion during I/Q data compression.

Sensing circuitry performs bit-vector multiplication inside the memory array, cutting bus transfer time and power use.

Rounded LSBs are remapped to MSB positions to expose matching floating-point patterns, improving cache bus compression and cutting transfer power.

Nearest-boundary decomposition maps constant multipliers into shift and add/subtract logic to cut PLD delay and LUT usage.

A control-bit-driven compression tree and adder combine checksum and fixed-point addition to cut circuit area and power.

Exponent-aligned mantissa combining lets DSPs execute floating-point complex multiply-add with lower latency, energy use, and logic area.

Redundant bidirectional routing lets adjacent DSP blocks bypass defects and stay stitched for larger, more precise operations.

A constant-based bit-shift approach extracts integer and fractional parts from floating-point data with fewer operations and lower latency.

Parallel MSB and LSB accumulation shortens DDS carry chains, cutting delay and circuit cost while preserving fine frequency resolution.

Run-based character mapping compacts mixed RFID and barcode strings, cutting bit use and speeding access over slow air interfaces.

A map-based hardware circuit compresses sparse data and reconstructs it quickly, cutting memory bandwidth demand and decompression cost.

Modified ternary adder logic improves FPGA generalized parallel counters by fitting routing limits while reducing logic element usage.

Carry-save arithmetic merges Viterbi and Turbo decoder operations to cut carry latency, hardware area, and power in radix-4 decoding.

Redundant routing lets adjacent DSP blocks bypass defects and stitch together for higher-precision, larger arithmetic operations.

Cascaded DSP elements use bitwise addition and carry-out handling to overcome PLD bottlenecks in real-time concurrent arithmetic.

Cascaded DSP elements with linked input register blocks ease PLD fabric bottlenecks and raise throughput for concurrent arithmetic tasks.

Dedicated pre-adder, multiplier, and ALU paths bypass PLD fabric bottlenecks to support faster concurrent real-time DSP arithmetic.

A sortable floating-point format preserves IEEE 754R values while enabling memcmp-style comparison and compressed storage on existing microprocessors.

Direct offset wire segments and buffers improve tile reachability in configurable IC carry chains while cutting redundant switching logic.

A carry bypass path and validity control let wide crypto adders scale with lower verification, layout, and timing-analysis effort.

A four-phase pipeline lets DCT/IDCT processors scale process elements to match performance needs while improving efficiency and controlling cost.

Direct links between FPGA DSP blocks let successive products be accumulated inside MAC circuitry, reducing logic use and overflow handling overhead.

Parallel register cells with one-at-a-time bus coupling simplify control and cut cross currents in large-operand calculating units.

A Radix-3 stage with shared FIFO RAM lets pipelined butterfly hardware handle 3×2N and 2N FFTs with less area and fewer passes.

Sign-based sum and difference paths let floating-point hardware cut power and silicon area while maintaining processing performance.

A unified multiplier handles sparse integer and floating-point neural operations, cutting chip area and power while preserving accuracy.

A two-stage adder and truncation scheme removes rounding bias in binary numbers while reducing hardware complexity in electronic circuits.

Optical clock delivery in a hybrid electro-photonic NoC cuts skew and power waste while improving data movement efficiency in ML processors.

Storing ML weights in sign-magnitude form cuts bit toggling during memory reads, then converts near compute units to lower power and heat.

Separate ALU execution paths let a GPU run mixed-precision math with higher dot-product accuracy and lower execution overhead.