A Non-conventional Sum-and-Max based Neural Network layer for Low Power Classification-Reference-Cited by-同舟云学术

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A Non-conventional Sum-and-Max based Neural Network layer for Low Power Classification

Published:2022-05-28 Issue: Volume: Page:
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Container-title:2022 IEEE International Symposium on Circuits and Systems (ISCAS)
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Author:

Prono Luciano¹,Mangia Mauro²,Pareschizy Fabio¹,Rovatti Riccardo¹,Settizy Gianluca²

Affiliation:

1. DET, Politecnico di Torino,Italy

2. University of Bologna,DEI,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9937201/9937203/09937576.pdf?arnumber=9937576

Reference24 articles.

1. Accelerating Deep Neural Networks with Analog Memory Devices

2. Enabling High-Performance DNN Inference Accelerators Using Non-Volatile Analog Memory (Invited)

3. A Case for Emerging Memories in DNN Accelerators

4. Quantized deep neural networks for energy efficient hardware-based inference

5. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding;han;arXiv 1510 00149 [cs],2016

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Aggressively prunable MAM²-based Deep Neural Oracle for ECG acquisition by Compressed Sensing;2022 IEEE Biomedical Circuits and Systems Conference (BioCAS);2022-10-13

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