An RRAM-Based Hierarchical Computing-in-Memory Architecture With Synchronous Parallelism for 3-D Point Cloud Recognition

Abstract

Point clouds have important applications, such as computer vision, autonomous driving, and robotics. However, point cloud recognition task encounters challenges related to data input equivalence and computational overhead in conventional hardware. To address these issues, we propose a Hierarchical Synchronous Parallel Architecture (HSPA) for resistive-random-access-memory (RRAM) based computing-in-memory (CIM), which significantly enhances the computational parallelism of point cloud data with DeepSets network. The point cloud processing is experimentally implemented in RRAM-based HSPA CIM hardware fabricated using a commercial 40 nm CMOS technology. The results indicate that the HSPA achieves a remarkable energy efficiency of 9.70 fJ/op and a high framerate of 145.4 fps (@200MHz) while maintaining a software-comparable accuracy of 98.4%.