Flexing RISC-V Instruction Subset Processors to Extreme Edge

Fuente: arXiv
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Hauptverfasser: Raisiardali, Alireza, Iordanou, Konstantinos, Kufel, Jedrzej, Gudimetla, Kowshik, Myny, Kris, Ozer, Emre
Format: Preprint
Veröffentlicht: 2025
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author Raisiardali, Alireza
Iordanou, Konstantinos
Kufel, Jedrzej
Gudimetla, Kowshik
Myny, Kris
Ozer, Emre
author_facet Raisiardali, Alireza
Iordanou, Konstantinos
Kufel, Jedrzej
Gudimetla, Kowshik
Myny, Kris
Ozer, Emre
contents This paper presents an automated approach for designing processors that support a subset of the RISC-V instruction set architecture (ISA) for a new class of applications at Extreme Edge. The electronics used in extreme edge applications must be area and power-efficient, but also provide additional qualities, such as low cost, conformability, comfort and sustainability. Flexible electronics, rather than silicon-based electronics, will be able to meet the above qualities. For this purpose, we propose a methodology for generating RISC-V instruction subset processors (RISSPs) tailored to these applications and implementing them as flexible integrated circuits (FlexICs). The methodology makes verification an integral part of the processor design by treating each instruction in the ISA as a discrete, fully functional, pre-verified hardware block. It automatically builds a custom processor by stitching together the instruction hardware blocks required by an application or a set of applications in a specific domain. We generate RISSPs using the proposed methodology for three extreme edge applications, and embedded applications from the Embench benchmark suite. When synthesized, RISSPs can achieve 8-to-43% reduction in area and 3-to-30% reduction in power compared to a processor supporting the full RISC-V ISA, and are also on average ~40 times more energy efficient than Serv - the world's smallest 32-bit RISC-V processor. When physically implemented as FlexICs, the three extreme edge RISSPs achieve up to 42% area and 21% power savings with respect to the full RISC-V processor.
format Preprint
id arxiv_https___arxiv_org_abs_2505_04567
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Flexing RISC-V Instruction Subset Processors to Extreme Edge
Raisiardali, Alireza
Iordanou, Konstantinos
Kufel, Jedrzej
Gudimetla, Kowshik
Myny, Kris
Ozer, Emre
Hardware Architecture
Emerging Technologies
This paper presents an automated approach for designing processors that support a subset of the RISC-V instruction set architecture (ISA) for a new class of applications at Extreme Edge. The electronics used in extreme edge applications must be area and power-efficient, but also provide additional qualities, such as low cost, conformability, comfort and sustainability. Flexible electronics, rather than silicon-based electronics, will be able to meet the above qualities. For this purpose, we propose a methodology for generating RISC-V instruction subset processors (RISSPs) tailored to these applications and implementing them as flexible integrated circuits (FlexICs). The methodology makes verification an integral part of the processor design by treating each instruction in the ISA as a discrete, fully functional, pre-verified hardware block. It automatically builds a custom processor by stitching together the instruction hardware blocks required by an application or a set of applications in a specific domain. We generate RISSPs using the proposed methodology for three extreme edge applications, and embedded applications from the Embench benchmark suite. When synthesized, RISSPs can achieve 8-to-43% reduction in area and 3-to-30% reduction in power compared to a processor supporting the full RISC-V ISA, and are also on average ~40 times more energy efficient than Serv - the world's smallest 32-bit RISC-V processor. When physically implemented as FlexICs, the three extreme edge RISSPs achieve up to 42% area and 21% power savings with respect to the full RISC-V processor.
title Flexing RISC-V Instruction Subset Processors to Extreme Edge
topic Hardware Architecture
Emerging Technologies
url https://arxiv.org/abs/2505.04567