A new technical paper titled “A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications” was published by researchers at University of Southern California (USC).
According to the paper, “we propose a novel Processing-in-Pixel-in-memory (P2M) 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 P2M 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.”
Find the technical paper here. Published August 2022.
Datta, G., Kundu, S., Yin, Z. et al. A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications. Sci Rep 12, 14396 (2022). https://doi.org/10.1038/s41598-022-17934-1 (open access link).
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