Abstract

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 (P2M) paradigm …

Metadata

publication

Scientific Reports 12 (1), 14396, 2022

year

2022

publication date

2022/8/23

authors

Gourav Datta, Souvik Kundu, Zihan Yin, Ravi Teja Lakkireddy, Joe Mathai, Ajey P Jacob, Peter A Beerel, Akhilesh R Jaiswal

link

https://www.nature.com/articles/s41598-022-17934-1

resource_link

https://www.nature.com/articles/s41598-022-17934-1

journal

Scientific Reports

volume

12

issue

1

pages

14396

publisher

Nature Publishing Group UK