Design Framework for SRAM-Based Computing-In-Memory Edge CNN Accelerators
Yi‐Min Wang, Zhuo Zou, Li‐Rong Zheng · 2021
This paper presents an architectural framework and an evaluation model for Static Random Access Memory (SRAM)-based Computing-in-Memory (CIM) edge Convolutional Neural Network (CNN) accelerators. To provide a baseline for system-level design perspectives, an architectural framework for SRAM-CIM design concerning the key design points in state-of-the-art works is proposed. Furthermore, a configurable evaluation model featuring top-down design flow based on the proposed framework is established to investigate design space explorations. Case studies validated the framework and evaluation model using LeNet-5, AlexNet and VGG-16 to achieve energy-aware optimizations. The optimized memory scale for LeNet-5 is "16 PEs and 120 tiles" with the minimal estimated inference energy of 0.0018J, while for AlexNet and VGG-16, "16 PEs and 120 tiles" is better achieving minimal energy consumption of 0.1733mJ and 0.6825mJ respectively. Estimation results highlight tradeoffs among data represent- tation parameters and memory partitioning parameters. This work provides specific SRAM-CIM design guidelines from a system-level perspective.