Novel In-Pixel-in-Memory (P2M) Paradigm for Edge Intelligence (USC)


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).


Related Reading
New Uses For AI In Chips
ML/DL is increasing design complexity at the edge, but it’s also adding new options for improving power and performance.
Using AI To Speed Up Edge Computing
Optimizing a system’s behavior can improve PPA and extend its useful lifetime.
Can Analog Make A Comeback?
The industry reached an inflection point where analog is getting a fresh look, but digital will not cede ground readily.

Leave a Reply

(Note: This name will be displayed publicly)