Single Instruction Multiple Data Multiprocessors TRIZ Cases

A bit-slice tile PHY uses top-metal bondpad configuration to support multiple protocols and bump pitches with one IC design.

Programmable processing elements and a memory-less interconnect improve 5G/6G signal processing throughput, power use, and chip area.

Double-buffered neuron memory reuses and shares DNN data to cut external reads, lower power use, and sustain throughput.

Inline vector quantization cuts NN weight memory transfer and lookup time by using dynamic vector lengths and codebook-based retrieval.

Virtualized hardware iterators cut neural network latency by parallelizing data traversal and reducing memory-management overhead.

A hardware data formatter gathers non-consecutive memory subsets for parallel array processing, cutting read latency and boosting throughput.

Calculated shifting bits let a directed line buffer pack NN data into contiguous blocks, cutting memory reads and writes for faster processing.

Skipping zero or near-zero input and weight operations cuts DNN processor power use while preserving throughput on limited-power hardware.

A line buffer shifts discontiguous memory blocks by calculated strides so neural networks can process data contiguously with fewer cycles and less latency.

Consecutive data subsets are gathered in hardware so computational arrays can process non-consecutive memory elements with lower latency and higher throughput.

Spare homogeneous subarrays and tagged unusable elements let a CGRA shift placement and routing around defects to preserve function and improve yield.

Shared neuron and line buffers cut repeated memory reads in DNN hardware, improving throughput while lowering power consumption.

Skipping zero or near-zero input and weight values cuts DNN processor power use while boosting throughput through cooperative neuron execution.

Dynamic data partition sequencing keeps more activations in local memory, cutting main-memory transfers, latency, and power in NN workloads.

Parallel kernel and input processing cut data reloads, bandwidth use, and power draw in DNN hardware with gated accumulators.

Padding neural network blob data to memory boundaries cuts read/write overhead and speeds processing with virtualized hardware iterators.

Bandwidth throttling at the DNN fabric interface smooths transaction flow to cut peak power use while keeping processing units productive.

Multiple processor tiles and switch-based routing improve parallel execution and memory access while limiting DMA transfers after cache misses.

Grouped loading of non-consecutive memory values cuts address calculations and read latency for higher computational array throughput.

Separate buffering of DMA and operation descriptors cuts DNN latency so the neural network module can finish sooner and power down earlier.

Layer descriptor dependencies and fence control cut DNN processing latency, enabling earlier power-down in low-power edge devices.

Mask-based compression of sparse activation data cuts memory bus traffic, helping DNN processors run faster with lower power use.

Virtualized hardware iterators improve neural network data traversal, cut latency, and ease memory bottlenecks across multi-dimensional blobs.

Inline vector dequantization cuts NN weight memory traffic and read latency while preserving accuracy through dynamic codebooks.

Shift-stride line buffering turns discontiguous memory blocks into contiguous neural network data, reducing write cycles, latency, and power use.

Large DMA transfers are split into smaller blocks so queued small transfers can run sooner, reducing idle time and improving transfer efficiency.

Virtualized hardware iterators cut neural network latency by improving data traversal, parallel processing, and memory use.

Independent tile switches and instruction streams improve parallel data transfer while cutting power use in reconfigurable ICs.

Cycle waits and transaction count limits smooth DNN fabric traffic, improving processing efficiency while reducing power consumption.

A DMA arbiter and fragmenter split large transfers so smaller queued blocks are serviced promptly, reducing starvation, idle time, and power use.

Dataflow graph overlay on lightweight processing elements cuts scheduling overhead and power while sustaining high HPC throughput.

Hardware 2MIN and 2MAX instructions replace branch-heavy heap operations to speed Huffman code generation on modern processors.

Segmented bus paths with bypass and access blocks improve FPGA logic-chip routing flexibility, module sizing, and signal delay control.

Embedded cache and coherent memory cut external DSP memory accesses, reducing stalls while sustaining parallel SIMD data processing.

Directional wavefront operand broadcast across overlapping patch meshes cuts interconnect congestion and aligns MAC execution to reduce stalls.

A 2D mesh of neighboring processing elements cuts data movement, reducing power use and processing time in neural network workloads.

Compute-balanced tiles, SIMD stripes, and banked buffers make sparse web-scale graph processing scalable despite irregular memory access.

Stacked 2D meshes with express links and hybrid bonding shorten wafer-scale interconnect paths to cut latency, energy use, and bandwidth loss.

Hardware accumulation and aggregation inside the interconnect cuts redundant memory transfers and improves gradient collection efficiency.

Channel-axis convolution lets the accelerator handle normalization on-chip, cutting data movement, memory access cost, latency, and power.

Context masks load only active CGRA state for each kernel, cutting context-switch latency and improving compute-near-memory throughput.

Shared SIMD and flow-control logic lets multiple cores offload data processing, cutting die area, power use, and hardware duplication.

A 2D pixel processing engine with local memory and adjustable bit width cuts SoC memory bottlenecks for image and video pipelines.

Separate load/store units and instruction mapping cut SIMD memory latency while preserving parallel processing efficiency and interoperability.

Atomic post-synchronization sequences let multi-tile GPUs cut cross-tile latency and overhead while preserving data consistency after kernel completion.

A dual-mode sense amplifier and big-little DRAM array cut read energy while enabling dense in-memory MAC and SIMD operations.

A 2D array of processing elements moves data through local registers to cut memory latency, reduce bottlenecks, and lower power use.

Parallel processing elements and a shared control unit raise machine learning data throughput while cutting overhead from traditional CPU and GPU execution.

Capture and validation APIs expose incomplete graph code locations, helping engineers manage dependencies and update CUDA graph execution.

A silicon bridge with ALUs for atomic operations helps GPUs handle diverse parallel workloads while improving resource use across graphics and compute tasks.

DMA and shared memory let DPE tiles move data between cores without latency-heavy core interfaces, improving memory use and throughput.

Calculating an advantage score determines whether extra-memory or in-memory circuits execute a task, mitigating memory access bottlenecks.

A security management system segments a multiprocessor into independent OS instances to provide individual processor security functions.