Combining Gradients and Probabilities for Heterogeneous Approximation of Neural Networks-Reference-Cited by-同舟云学术

Combining Gradients and Probabilities for Heterogeneous Approximation of Neural Networks

Published:2022-10-30 Issue: Volume: Page:
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Container-title:Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design
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Author:

Trommer Elias¹,Waschneck Bernd¹,Kumar Akash²

Affiliation:

1. Infineon Technologies, Dresden, Germany

2. Center for Advancing Electronics Dresden (cfaed), Dresden, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3508352.3549329

Reference41 articles.

1. Yoshua Bengio , Nicholas Léonard , and Aaron C . Courville . 2013 . Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. CoRR abs/1308.3432 (2013). Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville. 2013. Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. CoRR abs/1308.3432 (2013).

2. Nicholas Cheney , Martin Schrimpf , and Gabriel Kreiman . 2017. On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. CoRR abs/1703.08245 ( 2017 ). Nicholas Cheney, Martin Schrimpf, and Gabriel Kreiman. 2017. On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. CoRR abs/1703.08245 (2017).

3. Full Approximation of Deep Neural Networks through Efficient Optimization

4. ProxSim: GPU-based Simulation Framework for Cross-Layer Approximate DNN Optimization

5. Efficient Accuracy Recovery in Approximate Neural Networks by Systematic Error Modelling

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