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SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning

Published: 06 June 2022 Publication History

Abstract

While cycle-accurate simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigation. This work describes a concerted effort, where machine learning (ML) is used to accelerate microarchitecture simulation. First, an ML-based instruction latency prediction framework that accounts for both static instruction properties and dynamic processor states is constructed. Then, a GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor, and its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator. Leveraging modern GPUs, the ML-based simulator outperforms traditional CPU-based simulators significantly.

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems
Proceedings of the ACM on Measurement and Analysis of Computing Systems  Volume 6, Issue 2
POMACS
June 2022
499 pages
EISSN:2476-1249
DOI:10.1145/3543145
Issue’s Table of Contents
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 06 June 2022
Published in POMACS Volume 6, Issue 2

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  1. computer architecture simulation
  2. deep learning
  3. gpu

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