Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications

Albert Gural, Boris Murmann
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2515-2524, 2019.

Abstract

In the age of Internet of Things (IoT), embedded devices ranging from ARM Cortex M0s with hundreds of KB of RAM to Arduinos with 2KB RAM are expected to perform increasingly sophisticated classification tasks, such as voice and gesture recognition, activity tracking, and biometric security. While convolutional neural networks (CNNs), together with spectrogram preprocessing, are a natural solution to many of these classification tasks, storage of the network’s activations often exceeds the hard memory constraints of embedded platforms. This paper presents memory-optimal direct convolutions as a way to push classification accuracy as high as possible given strict hardware memory constraints at the expense of extra compute. We therefore explore the opposite end of the compute-memory trade-off curve from standard approaches that minimize latency. We validate the memory-optimal CNN technique with an Arduino implementation of the 10-class MNIST classification task, fitting the network specification, weights, and activations entirely within 2KB SRAM and achieving a state-of-the-art classification accuracy for small-scale embedded systems of 99.15%.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-gural19a, title = {Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications}, author = {Gural, Albert and Murmann, Boris}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {2515--2524}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/gural19a/gural19a.pdf}, url = {https://proceedings.mlr.press/v97/gural19a.html}, abstract = {In the age of Internet of Things (IoT), embedded devices ranging from ARM Cortex M0s with hundreds of KB of RAM to Arduinos with 2KB RAM are expected to perform increasingly sophisticated classification tasks, such as voice and gesture recognition, activity tracking, and biometric security. While convolutional neural networks (CNNs), together with spectrogram preprocessing, are a natural solution to many of these classification tasks, storage of the network’s activations often exceeds the hard memory constraints of embedded platforms. This paper presents memory-optimal direct convolutions as a way to push classification accuracy as high as possible given strict hardware memory constraints at the expense of extra compute. We therefore explore the opposite end of the compute-memory trade-off curve from standard approaches that minimize latency. We validate the memory-optimal CNN technique with an Arduino implementation of the 10-class MNIST classification task, fitting the network specification, weights, and activations entirely within 2KB SRAM and achieving a state-of-the-art classification accuracy for small-scale embedded systems of 99.15%.} }
Endnote
%0 Conference Paper %T Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications %A Albert Gural %A Boris Murmann %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-gural19a %I PMLR %P 2515--2524 %U https://proceedings.mlr.press/v97/gural19a.html %V 97 %X In the age of Internet of Things (IoT), embedded devices ranging from ARM Cortex M0s with hundreds of KB of RAM to Arduinos with 2KB RAM are expected to perform increasingly sophisticated classification tasks, such as voice and gesture recognition, activity tracking, and biometric security. While convolutional neural networks (CNNs), together with spectrogram preprocessing, are a natural solution to many of these classification tasks, storage of the network’s activations often exceeds the hard memory constraints of embedded platforms. This paper presents memory-optimal direct convolutions as a way to push classification accuracy as high as possible given strict hardware memory constraints at the expense of extra compute. We therefore explore the opposite end of the compute-memory trade-off curve from standard approaches that minimize latency. We validate the memory-optimal CNN technique with an Arduino implementation of the 10-class MNIST classification task, fitting the network specification, weights, and activations entirely within 2KB SRAM and achieving a state-of-the-art classification accuracy for small-scale embedded systems of 99.15%.
APA
Gural, A. & Murmann, B.. (2019). Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:2515-2524 Available from https://proceedings.mlr.press/v97/gural19a.html.

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