Physical Realisation TRIZ Cases

A DMA controller maps external memory addresses to internal cache locations, cutting neural network cache thrashing, latency, and off-chip traffic.

A bandwidth-aware scheduling scheme coordinates ReduceScatter, Allgather, and pipeline stages to speed neural network training.

Multiple injection points split query, pattern, cross-node, and control data to deliver customizable AI output with lower cost and skill demands.

A 1-D systolic OpenCL accelerator boosts FPGA CNN inference by scaling parallelism and supporting multiple models at runtime without recompilation.

Gradient-based interval segmentation shrinks quantization lookup tables for neural network operators while preserving lookup accuracy.

An interpreter neural network maps outputs between storage-resident models, reducing format mismatch delays and improving inter-model communication.

Room-temperature photodetectors and linear silicon photonics replace cryogenic counters and nonlinear devices to scale AI inference.

Partitioned weights and activations stay in on-chip memory across cores, cutting off-chip bandwidth use and speeding CNN execution.

Channel-wise quantization converts floating-point BatchNorm to fixed-point form while preserving accuracy and enabling efficient GEMM execution.

A lifetime-based memory plan assigns neural network buffers across primary and secondary memory to cut transfers and free more compute memory.

Bundling activation events with shared coordinates cuts message traffic and memory access in neural network processors.

A modified two-pass SoftMax replaces division with log and exponent steps to cut transformer runtime without losing accuracy.

A threshold-based tie-breaker layer resolves near-equal class weights to keep deep learning inference consistent across hardware stacks.

Current-direction control in synapse crossbars enables negative weights and backpropagation while cutting neuromorphic hardware size and power use.

A next read index and data cache reuse weight data to cut TCM access, lower power draw, and keep CNN processing continuous.

Selective reintroduction of pruned weights aligns memory sections so parallel neural cores run in lock step without stalls.

Per-tile scale calculation inside the compute unit avoids a second data pass, improving quantization speed and memory efficiency.

Higher precision is kept on residual-path activations while other activations use lower precision to preserve outliers and cut inference latency.

Dynamic satellite tasking, multi-source fusion, and containerized analytics improve collection timing, reduce waste, and speed intelligence delivery.

Intermediate shared-layer data lets a server refine terminal AI models without sending sensitive user data, preserving privacy and processing speed.

Ring tensor convolution cuts CNN inference complexity while avoiding irregular pruning flow, extra bitwidths, and hardware overhead.

An analog neural chip delivers early parallel output to a coupled digital ANN, improving inference speed without added synchronization burden.