The AI Front Page

Reading signals from this article are folded back into your front page ranking on this device.

Research/arXiv AI/ML/July 29, 2026 at 5:12 PM

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

A new arXiv AI paper by Harvey Samuel George Johnson and Sendy Phang studies Investigating reservoir computing for branch predictionin pipelined processors using emerging CMOS memristor devices.

ResearchAI
Research / arXiv AI/ML
Source

Follow arXiv AI/ML to make it a durable For You signal.

arXiv ID: 2607.27140v1 Title: Investigating reservoir computing for branch predictionin pipelined processors using emerging CMOS memristor devices Authors: Harvey Samuel George Johnson, Sendy Phang Primary category: cs.AR Categories: cs.AR, cs.CE, cs.ET, cs.LG, physics.app-ph Comment: 53 pages, 61 figures, Master of Engineering final project report, awarded Peter John Award Published: 2026-07-29T17:12:18Z Updated: 2026-07-29T17:12:18Z 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 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. PDF: https://arxiv.org/pdf/2607.27140v1

arXiv paper: Investigating reservoir computing for branch predictionin pipelined processors using emerging CMOS memristor devices | The AI Front Page