A camera that CNNs: Towards embedded neural networks on pixel processor arrays

Laurie Bose, Piotr Dudek, Jianing Chen, Stephen Carey, Walterio Mayol-Cuevas

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


We present a convolutional neural network implementation for pixel processor array (PPA) sensors. PPA hardware consists of a fine-grained array of general-purpose processing elements, each capable of light capture, data storage, program execution, and communication with neighboring elements. This allows images to be stored and manipulated directly at the point of light capture, rather than having to transfer images to external processing hardware. Our CNN approach divides this array up into 4x4 blocks of processing elements, essentially trading-off image resolution for increased local memory capacity per 4x4 'pixel'. We implement parallel operations for image addition, subtraction and bit-shifting images in this 4x4 block format. Using these components we formulate how to perform ternary weight convolutions upon these images, compactly store results of such convolutions, perform max-pooling, and transfer the resulting sub-sampled data to an attached micro-controller. We train ternary weight filter CNNs for digit recognition and a simple tracking task, and demonstrate inference of these networks upon the SCAMP5 PPA system. This work represents a first step towards embedding neural network processing capability directly onto the focal plane of a sensor.
Original languageEnglish
Title of host publication2019 IEEE/CVF International Conference on Computer Vision
Place of PublicationSeoul, Korea
Number of pages10
ISBN (Print)9781728148038
Publication statusPublished - Oct 2019

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


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