PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration-Reference-Cited by-同舟云学术

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PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration

Published:2023-07-09 Issue: Volume: Page:
ISSN:
Container-title:2023 60th ACM/IEEE Design Automation Conference (DAC)
language:
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Author:

Petri Richard¹,Zhang Grace Li²,Chen Yiran³,Schlichtmann Ulf¹,Li Bing¹

Affiliation:

1. Technical University of Munich

2. Technical University of Darmstadt

3. Duke University

Funder

Deutsche Forschungsgemeinschaft

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10247654/10247655/10247868.pdf?arnumber=10247868

Reference18 articles.

1. GreenTPU

2. SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks

3. Estimating or propagating gradients through stochastic neurons for conditional computation;bengio,2013

4. Minerva

5. UPTPU: Improving Energy Efficiency of a Tensor Processing Unit through Underutilization Based Power-Gating

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1. Early-Exit with Class Exclusion for Efficient Inference of Neural Networks;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22

2. Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks;2024 Design, Automation & Test in Europe Conference & Exhibition (DATE);2024-03-25

3. Logic Design of Neural Networks for High-Throughput and Low-Power Applications;2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC);2024-01-22

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