SuperSIM: a comprehensive benchmarking framework for neural networks using superconductor Josephson devices

Author:

Zhu GuangxianORCID,Kan Yirong,Zhang Renyuan,Nakashima Yasuhiko,Luo Wenhui,Takeuchi NaokiORCID,Yoshikawa Nobuyuki,Chen OliviaORCID

Abstract

Abstract This paper introduces SuperSIM, a benchmarking framework tailored for neural networks using superconducting Josephson devices, specifically focusing on Adiabatic Quantum Flux Parametron (AQFP) based Processing-in-Memory (PIM) architectures. Our framework offers in-depth architecture-level simulations and performance assessments to enhance AQFP PIM chip development. It supports single and multi-bit PIM designs, various AQFP memory cell types, and diverse clocking methods. Additionally, it integrates circuit-level models for precise energy, delay, and area measurements, ensuring accurate performance evaluation. The framework includes application, device, and architectural layers for versatile configurations and cycle-accurate energy, latency, and area simulations. Experiments validate our framework, with case studies on algorithm and architecture-level features, examining data precision, crossbar size, operating frequency and clocking scheme impacts on computational accuracy, energy use, overall latency and hardware cost.

Funder

Japan Society for the Promotion of Science

Fusion Oriented REsearch for disruptive Science and Technology

Advanced Low Carbon Technology Research and Development Program

Publisher

IOP Publishing

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