A 0.8 V Intelligent Vision Sensor With Tiny Convolutional Neural Network and Programmable Weights Using Mixed-Mode Processing-in-Sensor Technique for Image Classification

This article presents an intelligent vision sensor (IVS) with embedded tiny convolutional neural network (CNN) model and programmable processing-in-sensor (PIS) circuit for real-time inference applications of low-power edge devices. The proposed imager realizes the full computing functions of a customized three-layers tiny network, which includes a 3 \times 3 convolution layer (stride = 3) with activation function of rectified linear unit (ReLU), a 2 \times 2 maximum pooling (MP) layer (stride = 2), and a 1 \times 1 fully connected (FC) layer for inference. A 0.8 V 128 \times 128 IVS prototype was fabricated and verified in TSMC 0.18 \mu m standard CMOS technology. In normal image mode, it consumed 76.4 \mu W with full-resolution ( 126 \times 126 active resolution) image output at 125 f/s. In CNN mode, it consumed 134.5 \mu W at 250 f/s and an achieved iFoMs of 33.8 pJ/pixel \cdot frame. Using the proposed mixed-mode PIS circuits, the prototype is configured to demonstrate a "human face or not detection" task with an achieved accuracy of 93.6%.