The relentless growth of artificial intelligence of things (AIoT) applications has tightened sub-milliwatt powerbudget constraints on embedded vision systems. To address this challenge, this brief presents the first brain-inspired intelligent vision sensor (BIVS) based on a novel spiking processing-insensor (PIS) architecture that efficiently converts captured pulsewidth-modulation (PWM) image data into real-time sparse spike features for subsequent intelligent processing, offering an attractive balance between computational accuracy and energy efficiency. A 126 126 BIVS is designed in 180nm CMOS technology and consumes 66.9μJW at 661fps with a resulting efficiency of 5.02 TOPS/W and FoM of 6.37 pJ/pixel/frame. It also achieves a spike feature map accuracy of 99.73% on the MNIST dataset and a classification accuracy of 99.05% when coupled with a simplified spiking convolutional neural network (SCNN).

With the growing demand for energy-efficient convolutional neural network (CNN) accelerators in edge intelligence, conventional digital CNN processors with fixed precision incur excessive switching energy and limited scalability. This work presents a time-domain CNN (TD-CNN) engine that achieves adaptive precision and computation reduction for ultra-low-power operation. The main features include: 1) A dual-precision mode-switching DTC (DPMS-DTC) supporting both low-power and high-linearity modes. 2) A bit-sliced hybrid-precision (BSHP) MAC computing technique that achieves high accuracy with low power consumption. 3) A threshold-controllable near-negative prediction (TCNNP) technique aimed at reducing redundant MAC operations. Fabricated in a 55-nm CMOS process, the engine achieves 28.7–412.5 μW power consumption and a 255.7-993.3 TOPS/W figure-of-merit. It delivers 92.10%–90.91% CIFAR-10 accuracy using 6-bit inputs and weights. With a 1% accuracy allowance, TCNNP reduces MAC operations by 59%.