RTSA: An RRAM-TCAM based In-Memory-Search Accelerator for Sub-100 µs Collision Detection

Jiahao Sun, Fangxin Liu, Yijian Zhang, Li Jiang, Rui Yang

Abstract: Collision detection is a highly timing-consuming task in motion planning, which accounts for over 90% of the total calculation time. Previous hardware accelerators can hardly maintain fast computation speed in real time while supporting a large roadmap. In this work, we present RTSA, a novel in-memory-search collision detection accelerator, which achieves an impressive sub-100 µs response time for collision detection in 800 MB scale roadmaps. Such accelerator leverages an in-situ-search-enabled memory architecture, enabling massively parallel search operations. RTSA is powered by ternary content-addressable memories (TCAMs) based on large-scale non-volatile resistive random-access memory (RRAM) arrays. TCAM eliminates the need for extensive data transfer between memory and computing units, leading to significant energy and delay saving. Such accelerator well exceeds the speed requirement for collision detection (<1 ms), making it highly suitable for various applications, including robot motion planning in dynamic environment, manufacturing, and physical simulation.