SimNet: Computer Architecture Simulation using Machine Learning

• URL: http://arxiv.org/abs/2105.05821v1
• Date: Wed, 12 May 2021 17:31:52 GMT
• Title: SimNet: Computer Architecture Simulation using Machine Learning
• Authors: Lingda Li, Santosh Pandey, Thomas Flynn, Hang Liu, Noel Wheeler, Adolfy Hoisie
• Abstract summary: This work describes a concerted effort, where machine learning (ML) is used to accelerate discrete-event simulation. A GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor. Its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator.
• Score: 3.7019798164954336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 problems under investigation. This work describes a concerted effort, where machine learning (ML) is used to accelerate discrete-event simulation. First, an ML-based instruction latency prediction framework that accounts for both static instruction/architecture properties and dynamic execution context 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 simulators significantly.

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