ViTSen: Bridging Vision Transformers and Edge Computing With Advanced In/Near-Sensor Processing

Abstract

This letter introduces ViTSen , optimizing vision transformers (ViTs) for resource-constrained edge devices. It features an in-sensor image compression technique to reduce data conversion and transmission power costs effectively. Further, ViTSen incorporates a ReRAM array, allowing efficient near-sensor analog convolution. This integration, novel pixel reading, and peripheral circuitry decrease the reliance on analog buffers and converters, significantly lowering power consumption. To make ViTSen compatible, several established ViT algorithms have undergone quantization and channel reduction. Circuit-to-application co-simulation results show that ViTSen maintains accuracy comparable to a full-precision baseline across various data precisions, achieving an efficiency of ~3.1 TOp/s/W.