Instruction Analysis TRIZ Cases
- DEFLATE Symbol Decoding Using CAM and Single-Instruction Execution
A CAM-based execution unit decodes DEFLATE symbols in one instruction, cutting CPU instruction load and decoder software footprint.
- Programmable CRC Instruction Set for Multi-Polynomial Computation
A programmable CRC instruction accepts the polynomial as an operand, replacing dedicated circuits or lookup tables for flexible CRC computation.
- Primitive Block Memory Allocation for Tile-Based GPU Rendering
Variable-size primitive block storage lets tile-based GPUs keep untransformed geometry and avoid redundant rasterization work, cutting time and power.
- Virtual Idle Loop Wake-Up Using MTRIGGER Memory Writes
A new MTRIGGER instruction replaces hypervisor round trips and IPIs with direct memory writes for lower-latency VM idle wake-up.
- Programmable Fabric ISA Integration for Low-Latency Coherent Offload
Embedding programmable fabric into the processor ISA cuts offload latency, preserves cache coherency, and enables flexible workload acceleration.
- MPC Processor and Compiler for Low-Overhead Secure Computation
A bespoke MPC processor and compiler cut computation and communication overhead in secure multiparty computation.
- Vector Coprocessor Scheduling to Hide Load Latency
A vector register scoreboard and time counter statically dispatch vector loads to hide latency without costly register renaming.
- 52-Bit Fused Integer Instructions for Faster HE NTT Arithmetic
Packed 52-bit multiply-add and subtract instructions cut NTT overhead in homomorphic encryption and speed inverse NTT execution.
- Vector Packed Concatenate-Shift for Modular Multiplication
Dual-source field concatenation and bit shifting cut instruction overhead and storage in vector modular multiplication, improving efficiency.
- Vector Processing Unit Micro-Op Cache for Out-of-Order Execution
A separate micro-op cache lets a vector processing unit split instructions out of order while cutting operation cache power and area.
- Sparse-Dense Matrix Accelerator Using Compressed Nonzero Indexing
Compressed nonzero values and position indexing let this SDMM accelerator skip zero elements and raise matrix multiply throughput.
- Asymmetric SIMD FMA Instructions for Low-Precision Deep Learning
Variable-precision SIMD FMA handles asymmetric 8-, 4-, 2-, and 1-bit operands to raise deep learning throughput beyond symmetric instructions.
- Docker Image Manifest Analysis for Instruction Vulnerability Checks
Manifest-level instruction analysis classifies Docker image files and checks CVE exposure beyond installed package scanning.