Web: http://arxiv.org/abs/2201.05842

Jan. 24, 2022, 2:11 a.m. | Igor Fedorov, Ramon Matas, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul Whatmough

cs.LG updates on arXiv.org arxiv.org

Emerging Internet-of-things (IoT) applications are driving deployment of
neural networks (NNs) on heavily constrained low-cost hardware (HW) platforms,
where accuracy is typically limited by memory capacity. To address this TinyML
challenge, new HW platforms like neural processing units (NPUs) have support
for model compression, which exploits aggressive network quantization and
unstructured pruning optimizations. The combination of NPUs with HW compression
and compressible models allows more expressive models in the same memory
footprint.


However, adding optimizations for compressibility on top of …

arxiv models tinyml

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