Computation Using Non-Contact Making Devices TRIZ Cases

Fusing depthwise and pointwise convolution kernels within a compute-in-memory array to process pre-activations directly in memory.

Half adder logic generates carry bits for most significant positions, enabling rounding increment injection before final summation to reduce cycle time.

A carry-save multiply-and-accumulate unit processes signals in carry-save format to reduce propagation delay.

A computational system performs Newton-Raphson iterations to produce results with additional accuracy bits for subsequent analysis.

A truncated summation array performs division by discarding less significant columns to reduce circuit area.

Segmented latch circuits synchronize odd and even data paths to resolve accumulation speed bottlenecks in deep learning MAC operations.

A neural network device uses convolution SRAM and diagonal accumulation SRAM to process sparse weight matrices efficiently.

A multiply-accumulate unit sums signals after parasitic capacitor transients stabilize.

Task entanglement-based coding encodes distributed matrices using Chebyshev polynomials to enable partial result recovery from edge devices.

An ontology decomposes user requirements into component-level functionalities to automate service configuration generation.

Expansion markers route composite data elements to standard computing operations, eliminating the need for specialized processing blocks.