Energy Efficiency Through In-Sensor Computing: ADC-less Real-Time Sensing for Image Edge Detection

Abstract

In-sensor computing has revolutionized modern vision-based applications, particularly in scenarios like autonomous vehicles and robotics where real-time or near-real-time processing is crucial. By enabling data processing at the sensor level, in-sensor computing eliminates the need to transmit data to cloud servers, significantly reducing latency and enhancing decision-making speed. Central to the in-sensor computing paradigm, CMOS image sensors (CISs) with edge computing, play a pivotal role in machine vision applications. The need for high resolution, low power, and real-time operation aligns seamlessly with the demands of modern vision-based applications. In this paper, we propose a novel approach for real-time image edge detection with an in-sensor, ADC-less sensing solution that achieves high energy efficiency and speed. The design utilizes the column-parallel architecture of existing CIS and the row-wise pixel readout scheme. Column voltages of three consecutive rows with a delay arrangement extract 4-bit edge pixels without deriving the actual digital image pixels. A time-to-digital conversion (TDC) technique using a 4-bit counter eliminates the requirement of power-hungry ADC. A 256(H) x 256(V) 2D CMOS pixel array with 10 μm pixel pitch is simulated using Spectre in TSMC 65nm low-power technology. CMOS pixels with wide dynamic range (WDR) capture the light intensity variation up to 92dB [10]. Simulation results show energy consumption of 2pW per pixel per frame, operating at a frame rate of 3.9kfps, all well-contained within a modest 0.5 mW power budget. The resultant frame rate emerges as notably superior in terms of speed, accompanied by a more than tenfold reduction in power consumption per edge frame-pixel compared to the existing prior art.