Enabling mixed-precision quantized neural networks in extreme-edge devices-Reference-Cited by-同舟云学术

Enabling mixed-precision quantized neural networks in extreme-edge devices

Published:2020-05-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Computing Frontiers
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Author:

Bruschi Nazareno¹,Garofalo Angelo¹,Conti Francesco¹,Tagliavini Giuseppe¹,Rossi Davide¹

Affiliation:

1. University of Bologna, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3387902.3394038

Reference9 articles.

1. PULP-NN: accelerating quantized neural networks on parallel ultra-low-power RISC-V processors

2. Bert Moons et. al. 2017. Minimum Energy Quantized Neural Networks. arXiv:cs.NE/1711.00215 Bert Moons et. al. 2017. Minimum Energy Quantized Neural Networks. arXiv:cs.NE/1711.00215

3. GAP-8: A RISC-V SoC for AI at the Edge of the IoT

4. Itay Hubara et. al. 2016. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. arXiv:cs.NE/1609.07061 Itay Hubara et. al. 2016. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. arXiv:cs.NE/1609.07061

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