An Isotropic Shift-Pointwise Network for Crossbar-Efficient Neural Network Design

Ziyi Guan, Boyu Li, Yuan Ren, Muqun Niu, Hantao Huang, Graziano Chesi, Hao Yu, Ngai Wong

Abstract: Resistive random-access memory (RRAM), with its programmable and nonvolatile conductance, permits compute-in-memory (CIM) at a much higher energy efficiency than the traditional von Neumann architecture, making it a promising candidate for edge AI. Nonetheless, the fixed-size crossbar tiles on RRAM are inherently unfit for conventional pyramid-shape convolutional neural networks (CNNs) that incur low crossbar utilization. To this end, we recognize the mixed-signal (digital-analog) nature in RRAM circuits and customize an isotropic shift-pointwise network that exploits digital shift operations for efficient spatial mixing and analog pointwise operations for channel mixing. To fast ablate various shift-pointwise topologies, a new recon-figurable energy-efficient shift module is designed and packaged into a seamless mixed-domain simulator. The optimized design achieves a near-100% crossbar utilization, providing a state-of-the-art INT8 accuracy of 94.88% (76.55%) on the CIFAR-10 (CIFAR-100) dataset with 1.6M parameters, which sets a new standard for RRAM-based AI accelerators.