A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications

The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such cameras are usually captured in analog voltages by a sensor pixel array, and then converted to the digital domain for subsequent AI processing using analog-to-digital converters (ADC). Recent research has tried to take advantage of massively parallel low-power analog/digital computing in the form of near- and in-sensor processing, in which the AI computation is performed partly in the periphery of the pixel array and partly in a separate on-board CPU/accelerator. Unfortunately, high-resolution input images still need to be streamed between the camera and the AI processing unit, frame by frame, causing energy, bandwidth, and security bottlenecks. To mitigate this problem, we propose a novel Processing-in-Pixel-in-memory ((PM)-M-2) paradigm, that customizes the pixel array by adding support for analog multi-channel, multi-bit convolution, batch normalization, and Rectified Linear Units (ReLU). Our solution includes a holistic algorithm-circuit co-design approach and the resulting (PM)-M-2 paradigm can be used as a drop-in replacement for embedding memory-intensive first few layers of convolutional neural network (CNN) models within foundry-manufacturable CMOS image sensor platforms. Our experimental results indicate that (PM)-M-2 reduces data transfer bandwidth from sensors and analog to digital conversions by similar to 21x, and the energy-delay product (EDP) incurred in processing a MobileNetV2 model on a TinyML use case for visual wake words dataset (VWW) by up to similar to 11x compared to standard near-processing or in-sensor implementations, without any significant drop in test accuracy.