SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning
This work addresses the time-to-solution bottleneck for computer architecture researchers and developers, offering a high-performance alternative to traditional simulators.
The paper tackles the slow speed of discrete-event computer architecture simulators by using machine learning to predict instruction latencies and implementing a GPU-accelerated parallel simulator, achieving significant performance improvements over state-of-the-art simulators.
While discrete-event 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 discrete-event 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 simulators significantly.