INSPIRE: In-Sensor Compressed Weight Retrieval for Enhancing ViT Efficiency at Edge

Deploying Vision Transformer (ViT) models on edge devices poses significant challenges due to the high bandwidth, energy demands, and latency associated with transmitting large weight parameter sets to the sensing unit, along with limited on-chip memory resources, which are often insufficient for storing these parameters. To address these constraints, we present a software-hardware co-design framework that incorporates a novel in-sensor Compressed Weight Retrieval mechanism within an intelligent vision sensor. This framework offers two key contributions. First, we propose an innovative hardware-friendly weight compression algorithm that substantially reduces bandwidth and power consumption by optimizing on-chip memory usage for storing weight parameters. Second, we leverage the exceptional efficiency of Silicon Photonic (SiPh) devices and design a novel in-sensor accelerator called INSPIRE for the first time to perform in-sensor retrieval of the compressed weights and parallel fine-grained convolution operations next to the pixel array, enabling low-power adaptable ViT inference on resource-constrained edge platforms. Our extensive simulation results show that INSPIRE can remarkably reduce the memory footprint of ViT results with favorable accuracy. Besides, INSPIRE significantly reduces the bandwidth and power requirements associated with storing weight parameters in on-chip memory. INSPIRE achieves up to 245.4 Kilo FPS/W and reduces the data transfer energy by a factor of ∼11× on average compared with 4-bit quantized ViTs.