Building an Event-Driven Timing Simulator for Embedded Hybrid GPU-AI Accelerator

Abstract

The current trend towards the integration of artificial intelligence (AI) and graphics processing unit (GPU) technologies has resulted in the development of embedded hybrid GPU-AI accelerators, which offer high computational power and energy efficiency. One of the key challenges in designing such accelerators is ensuring their timing correctness, as any timing violation may lead to system failure and incorrect results. To address this challenge, timing simulators have been proposed as a promising solution, as they enable accurate and efficient timing analysis of these complex systems. Nevertheless, such simulators have their limitations: detailed ones, such as cycle-accurate simulators, have high accuracy but exhibit high overhead, while faster approaches, like event-driven simulators, usually cannot achieve high accuracy levels. Therefore, the question arises: How can we implement a timing simulator to achieve a balanced trade-off between accuracy and execution time?

In this context, we introduce NEOX-V, a cutting-edge RISC-V based GPU Processor optimized for GPGPU and AI workloads. However, the current NEOX-V product lacks timing information in its simulator. This has prompted us to use it as a case study to bridge the gap between accuracy and execution time in timing simulators. This is achieved through the implementation of a new feature: a timing simulator that utilizes event-driven modeling.

To assess the proposed simulator's accuracy and effectiveness, we employ a comprehensive validation framework, using diverse workloads and configurations, from simple micro-benchmarks to intricate AI tests. The results demonstrate that the timing simulator achieves an accuracy error below 8\% when compared to the RTL equivalent for all applications, with a marginal increase in actual simulation time of only 0.7\%. It is worth noting that the timing simulator's utility extends beyond predicting execution time; it also plays a crucial role in verifying the existing design and uncovering its limitations.

Overall, this thesis makes a significant contribution to the field of computer architecture by providing a powerful tool for the design, development, and evaluation of an embedded hybrid GPU-AI accelerator called NEOX-V. It is our hope that this work will inspire further research and development in this exciting and rapidly evolving field.