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Research PaperResearchia:202607.30069

Investigating reservoir computing for branch predictionin pipelined processors using emerging CMOS memristor devices

Harvey Samuel George Johnson

Abstract

This project aimed to develop a novel reservoir compute (RC) implementation framework targeting high-speed operation and integration with CMOS digital logic. With the target workload of branch prediction (BP) for multistage pipelined central pro-cessing unit (CPU) cores. For this, a novel memristor based RC design framework was developed within the context of the workload requirements. This was then implemented in simulation using industry standard modelling languages of System Verilog (SV) and ...

Submitted: July 30, 2026Subjects: Machine Learning; Data Science

Description / Details

This project aimed to develop a novel reservoir compute (RC) implementation framework targeting high-speed operation and integration with CMOS digital logic. With the target workload of branch prediction (BP) for multistage pipelined central pro-cessing unit (CPU) cores. For this, a novel memristor based RC design framework was developed within the context of the workload requirements. This was then implemented in simulation using industry standard modelling languages of System Verilog (SV) and Verilog-AMS (VAMS).The developed RC design framework was subsequently verified using a basic sequence detection task before further benchmarking for its effectiveness at BP. The developed RC framework was tested using the Dhrystone performance benchmark, while targeting the RISC-V RV64GC instruction set architecture (ISA). Conducted testing demonstrates that RC shows great promise for ap-plication to BP and is capable of achieving impressive overall prediction accuracy. However, testing also shows that further refinement of the developed RC design framework is necessary to address shortfalls in the adaptability of the proposed RC system. As comparison against the state of the art TAGE predictor showed the proposed RC design framework to be 15x slower to adapt to changes in branching behaviour.


Source: arXiv:2607.27140v1 - http://arxiv.org/abs/2607.27140v1 PDF: https://arxiv.org/pdf/2607.27140v1 Original Link: http://arxiv.org/abs/2607.27140v1

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Submission Info
Date:
Jul 30, 2026
Topic:
Data Science
Area:
Machine Learning
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