A 73.53TOPS/W 14.74TOPS Heterogeneous RRAM In-Memory and SRAM Near-Memory SoC for Hybrid Frame and Event-Based Target Tracking

Abstract

Vision-based high-speed target-identification and tracking is a critical application in unmanned aerial vehicles (UAV) with wide military and commercial usage. Traditional frame cameras processed through convolutional neural networks (CNN) exhibit high target-identification accuracy but with low throughput (hence low tracking speed) and high power. On the other hand, event cameras or dynamic vision sensors (DVS) generate a stream of binary asynchronous events corresponding to the changing intensity of the pixels capturing high-speed temporal information, characteristic of high-speed tracking. Such event streams with high spatial sparsity processed with bio-mimetic spiking neural networks (SNN) provide low power consumption and high throughput. However, the accuracy of object detection using such event cameras and SNNs is limited. Thus, a frame pipeline with a CNN and an event pipeline with a SNN (Fig. 29.5.1) possess complementary strengths in capturing and processing the spatial and temporal details, respectively. Hence, a hybrid network that fuses frame data processed using a CNN pipeline with event data processed through an SNN pipeline provides a platform for high-speed, high-accuracy and low-power target-identification and tracking. To address this need, we present a fully-programmable heterogeneous ARM Cortex-based SoC with an in-memory low-power RRAM-based CNN and a near-memory high-speed SRAM-based SNN in a hybrid architecture with applications in high-speed target identification and tracking.