FADESIM: Enable Fast and Accurate Design Exploration for Memristive Accelerators Considering Nonidealities

Bing Wu, Yibo Liu, Jinpeng Liu, Huan Cheng, Xueliang Wei, Wei Tong, Dan Feng

Abstract: Memristive accelerators (MAs), built with memristive crossbar arrays (MCAs), have gained significant attention for their ability to efficiently perform matrix-vector multiplication in diverse applications. Modeling and simulation are indispensable tools for exploring and evaluating architectural design. Specifically, for MAs, maintaining high computation accuracy has been challenging because of realistic nonidealities like wire resistance (i.e., IR drop), I–V nonlinearity, program variation, and so on. Thus, for the system architects, fast and exact analysis of the effects of nonidealities is highly desirable, especially during the vast early design space exploration of the architecture. However, the SPICE model and existing MCA compact models (CMs) do not offer acceptable speeds for accurate simulation purposes, making them less practical. Additionally, the existing MCA simplified circuit models and predictive models fail to produce effective results due to the complexity of IR drop, let alone the coupling of multiple nonidealities. To enable fast and accurate design exploration for MAs with nonidealities considered, in this article, we propose FADESIM which includes fast IR drop simulation methods and a processing chain for the joint simulation of multiple nonidealities. Starting with the analysis of the accurate CM for IR drop, we explore the special properties of the model to enable fast iterative methods as well as a method to skip invalid calculations. This significantly reduces the time complexity of the simulation from naive $O(n^{6})$ to $O(n^{3})$ . For less severe IR drop cases, a custom iterative update algorithm is presented for faster simulations as a supplement, specifically with a time complexity of near $O(n^{2})$ and proven applicable conditions. To simulate multiple nonidealities, we introduce a processing chain to inject corresponding processing functions before, during, and after the proposed fast IR drop simulation process according to the stages at which nonidealities take effect. The array-level experimental results show that our method achieves accurate simulations, with $19.8 \times - 884.9 \times $ faster and $276.7 \times - 8018.8 \times $ reduced memory usage compared to SPICE simulation. Further experiments at the algorithm-level demonstrate the effectiveness of our method in assisting architects with evaluating their designs.