Stream Semantic Registers: A Lightweight RISC-V ISA Extension Achieving Full Compute Utilization in Single-Issue Cores
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Abstract (arXiv)
Single-issue processor cores are very energy efficient but suffer from the von Neumann bottleneck, in that they must explicitly fetch and issue the loads/storse necessary to feed their ALU/FPU. Each instruction spent on moving data is a cycle not spent on computation, limiting ALU/FPU utilization to 33% on reductions. We propose "Stream Semantic Registers" to boost utilization and increase energy efficiency. SSR is a lightweight, non-invasive RISC-V ISA extension which implicitly encodes memory accesses as register reads/writes, eliminating a large number of loads/stores. We implement the proposed extension in the RTL of an existing multi-core cluster and synthesize the design for a modern 22nm technology. Our extension provides a significant, 2x to 5x, architectural speedup across different kernels at a small 11% increase in core area. Sequential code runs 3x faster on a single core, and 3x fewer cores are needed in a cluster to achieve the same performance. The utilization increase to almost 100% in leads to a 2x energy efficiency improvement in a multi-core cluster. The extension reduces instruction fetches by up to 3.5x and instruction cache power consumption by up to 5.6x. Compilers can automatically map loop nests to SSRs, making the changes transparent to the programmer.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Nov 2019.
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Citing papers per year: 2024: 1; 2025: 5; 2026: 1.
7 papers.
- 2025VEXP: A Low-Cost RISC-V ISA Extension for Accelerated Softmax Computation in Transformerscited by 4
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- 2025Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollerscited by 2
- 2025MXDOTP: A RISC-V ISA Extension for Enabling Microscaling (MX) Floating-Point Dot Productscited by 1
- 2025Empowering Vector Architectures for ML: The CAMP Architecture for Matrix Multiplicationcited by 0
- 2025PerfDojo: Automated ML Library Generation for Heterogeneous Architecturescited by 0
- 2026At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inferencecited by 0
- 2026At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference28 Jul 2026
- 2025PerfDojo: Automated ML Library Generation for Heterogeneous Architectures5 Nov 2025
- 2025MXDOTP: A RISC-V ISA Extension for Enabling Microscaling (MX) Floating-Point Dot Products19 May 2025
- 2025VEXP: A Low-Cost RISC-V ISA Extension for Accelerated Softmax Computation in Transformers15 Apr 2025
- 2025Empowering Vector Architectures for ML: The CAMP Architecture for Matrix Multiplication10 Apr 2025
- 2025Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollers8 Mar 2025
- 2024Efficient transformer adaptation for analog in-memory computing via low-rank adapters26 Nov 2024