NAX-Reference-Cited by-同舟云学术

NAX

Published:2022-07-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 59th ACM/IEEE Design Automation Conference
language:
Short-container-title:

Author:

Negi Shubham¹,Chakraborty Indranil¹,Ankit Aayush²,Roy Kaushik¹

Affiliation:

1. Purdue University

2. Microsoft Corporation

Funder

Semiconductor Research Corporation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3489517.3530476

Reference18 articles.

1. Mohamed S Abdelfattah et al. 2020. Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator. arXiv preprint arXiv:2002.05022 (2020). Mohamed S Abdelfattah et al. 2020. Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator. arXiv preprint arXiv:2002.05022 (2020).

2. Aayush Ankit et al. 2019 . PUMA: A programmable ultra-efficient memristor-based accelerator for machine learning inference. In ASPLOS. 715--731. Aayush Ankit et al. 2019. PUMA: A programmable ultra-efficient memristor-based accelerator for machine learning inference. In ASPLOS. 715--731.

3. Han Cai et al. 2018 . Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332 (2018). Han Cai et al. 2018. Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332 (2018).

4. Indranil Chakraborty et al. 2020 . Geniex: A generalized approach to emulating non-ideality in memristive xbars using neural networks. In 2020 DAC. IEEE , 1--6. Indranil Chakraborty et al. 2020. Geniex: A generalized approach to emulating non-ideality in memristive xbars using neural networks. In 2020 DAC. IEEE, 1--6.

5. Kanghyun Choi et al. 2020 . DANCE: Differentiable Accelerator/Network Co-Exploration. arXiv preprint arXiv:2009.06237 (2020). Kanghyun Choi et al. 2020. DANCE: Differentiable Accelerator/Network Co-Exploration. arXiv preprint arXiv:2009.06237 (2020).

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