RV-VP2: Unlocking the Potential of RISC-V Packed-SIMD for Embedded Processing

Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review

Contributors

Abstract

The RISC-V instruction set architecture (ISA) is increasingly gaining traction as an open-source standard, enabling the development of a variety of processor designs. These designs are capable of addressing a broad spectrum of application needs, spanning from embedded systems to high-performance computing. The RISC-V ISA features a modular structure, offering a selection of base ISAs and standardized extensions for customization. In this work, we present the first and open-source extension of a SystemC TLM-based RISC-V Virtual Prototype (VP) which supports the RISC-V “P” Packed-SIMD Version 0.9.11-draft-20211209 called RV-VP2. The RISC-V VP is an open-source simulation platform in SystemC TLM that supports RV32GC and RV64GC. The RISC-V VP is extended for the P-extension of the RISC-V for RV32 specification to focus on simulating embedded application scenarios. A total of 243 instructions are added in the RISC-V VP simulator, which belongs to the mandatory specification of P-extension for RV32. The extended RISC-V VP simulator is evaluated for different test cases; matrix multiplication, convolution, max-pooling, and fully connected layer, to demonstrate the efficiency of P-extension in comparison to base ISA. A convolution neural network called Lenet-5 is also used as a test case to compare performance with the base RV32IM. The results show a maximum speed-up of 5.87× and 2.97× when using P-extension for a 4-way (8-bit) SIMD and a 2-way (16-bit) SIMD for matrix multiplication respectively. For Lenet-5 a speed-up of 4.08× when using P-extension (4-way (8-bit) SIMD) was observed.

Details

Original languageEnglish
Title of host publicationEmbedded Computer Systems
EditorsLuigi Carro, Francesco Regazzoni, Christian Pilato
PublisherSpringer Science and Business Media B.V.
Pages59-71
Number of pages13
ISBN (electronic)978-3-031-78380-7
ISBN (print)978-3-031-78379-1
Publication statusPublished - Jan 2025
Peer-reviewedYes

Publication series

SeriesLecture Notes in Computer Science
Volume15227 LNCS
ISSN0302-9743

Conference

Title24th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation
Abbreviated titleSAMOS 2024
Conference number24
Duration29 June - 4 July 2024
Website
LocationDoryssa Seaside Resort
CityPythagorion
CountryGreece

External IDs

ORCID /0000-0003-2571-8441/work/223382086
ORCID /0000-0002-8019-7936/work/223382297

Keywords

Keywords

  • Deep Learning, Instruction Set Architecture, Machine Learning, RISC-V VP, Simulator, SystemC TLM