SCIMITAR: Stochastic Computing In-Memory In-Situ Tracking ARchitecture for Event-Based Cameras

Event-based cameras offer low latency and high-dynamic range imaging data in a sparse format that is well-suited for high-speed object tracking. Processing this sparse data in the same way as traditional camera data requires a great deal of unnecessary computation, making it difficult to take advantage of the high-effective frame rate for real-time processing. In this work, we propose an accelerator for high-speed object tracking on event-based camera data. SCIMITAR combines digital in-memory stochastic computing, in-situ stochastic stream generation, and multiple optimizations for utilizing input sparsity. SCIMITAR provides unparalleled performance with latency and energy that scale with sparsity. We demonstrate SCIMITAR performance on an object tracking application using circuit-level simulations of custom-designed compute-in-memory (CIM) macros and digital circuits. We achieve a frame processing rate of 26k frames/s with 100 regions-of-interest per frame and equivalent or better than state-of-the-art tracking accuracy. The accelerator achieves a peak throughput of 71 TOP/S and energy efficiency of 733 to 1702 TOP/S/W demonstrated on a range of event-based vision datasets, which is $5\times $ higher than other CIM solutions.

I. Introduction

Event-based cameras [1] transmit information about brightness changes as an asynchronous event stream. The characteristics of these cameras make them preferable to frame-based cameras for applications, such as object tracking [1], [2].