PointCIM: A Computing-in-Memory Architecture for Accelerating Deep Point Cloud Analytics

Efficient deep point cloud (PC) analytics is crucial for numerous emerging applications such as autonomous vehicles and augmented and virtual reality. Our roofline model analysis reveals that the “memory wall” bottleneck primarily constrains the execution efficiency of deep PC analytics, providing valuable insight into optimization opportunities. In contrast to previous works, which greatly rely on approximating the original algorithm to fit hardware limitations, the approach presented in this paper is analytical; that is, our approach does not require any modification to the original algorithm, thus preserving its integrity and accuracy. In this paper, we introduce PointCIM, the first deep PC analytics accelerator that leverages computing-in-memory (CIM) optimization opportunities to address memory inefficiency. We identify that existing in-memory methods cannot fully support the distance function required by PC network inference. To address the challenge, we propose computation optimizations, including the Base+Offset mapping and early stopping for bit-serial computation, not only to enable full support for PC network inference in memory, but also to significantly improve hardware efficiency. We design the CIM architecture support for the proposed computation optimizations, including the memristor crossbar architecture, custom peripheral logic, data layout, and pipelined execution. Evaluation results show that the designed accelerator provides an average speedup of 17.1× and an energy reduction of 9.6× compared to the baseline of a typical edge SoC. We also compare PointCIM with several state-of-the-art PC accelerators, yielding up to 10.7× speedup and 4.9× energy savings.