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Extending the RISC-V ISA for exploring advanced reconfigurable SIMD instructions

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CARRV2021_paper_86_Papaphilippou.pdfWorking paper631.17 kBAdobe PDFView/Open
Title: Extending the RISC-V ISA for exploring advanced reconfigurable SIMD instructions
Authors: Papaphilippou, P
Kelly, PHJ
Luk, W
Item Type: Working Paper
Abstract: This paper presents a novel, non-standard set of vector instruction types for exploring custom SIMD instructions in a softcore. The new types allow simultaneous access to a relatively high number of operands, reducing the instruction count where applicable. Additionally, a high-performance open-source RISC-V (RV32 IM) softcore is introduced, optimised for exploring custom SIMD instructions and streaming performance. By providing instruction templates for instruction development in HDL/Verilog, efficient FPGA-based instructions can be developed with few low-level lines of code. In order to improve custom SIMD instruction performance, the softcore's cache hierarchy is optimised for bandwidth, such as with very wide blocks for the last-level cache. The approach is demonstrated on example memory-intensive applications on an FPGA. Although the exploration is based on the softcore, the goal is to provide a means to experiment with advanced SIMD instructions which could be loaded in future CPUs that feature reconfigurable regions as custom instructions. Finally, we provide some insights on the challenges and effectiveness of such future micro-architectures.
Issue Date: 17-Jun-2021
URI: http://hdl.handle.net/10044/1/90082
Copyright Statement: © 2021 The Author(s)
Sponsor/Funder: Dunnhumby Limited
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: PO: 250130012887
EP/P010040/1
Keywords: FPGAs
RISC-V
softcore
SIMD
cache hierarchy
reconfigurable
custom instructions
big data
streaming
sorting
prefix scan
Notes: Accepted at the Fifth Workshop on Computer Architecture Research with RISC-V (CARRV 2021), co-located with ISCA 2021
Publication Status: Accepted
Appears in Collections:Computing