At-scale assessment of weight clustering for energy-efficient object detection accelerators-Reference-Cited by-同舟云学术

At-scale assessment of weight clustering for energy-efficient object detection accelerators

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing
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Author:

Caro Martí¹,Tabani Hamid²,Abella Jaume³

Affiliation:

1. Barcelona Supercomputing Center (BSC) and Universitat Politécnica de Catalunya (UPC)

2. Barcelona Supercomputing Center (BSC)

3. Universitat Politécnica de Catalunya (UPC)

Funder

Ministerio de Ciencia e Innovación

European Regional Development Fund

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477314.3507161

Reference22 articles.

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3. Yoojin Choi etal 2016. Towards the Limit of Network Quantization. CoRR abs/1612.01543 (2016). arXiv:1612.01543 http://arxiv.org/abs/1612.01543 Yoojin Choi et al. 2016. Towards the Limit of Network Quantization. CoRR abs/1612.01543 (2016). arXiv:1612.01543 http://arxiv.org/abs/1612.01543

4. I.S. Dhillon and D.S. Modha. 2000. A Data-Clustering Algorithm on Distributed Memory Multiprocessors. In Large-Scale Parallel Data Mining. Springer Berlin Heidelberg 245--260. I.S. Dhillon and D.S. Modha. 2000. A Data-Clustering Algorithm on Distributed Memory Multiprocessors. In Large-Scale Parallel Data Mining. Springer Berlin Heidelberg 245--260.

5. Y. Gong etal 2014. Compressing Deep Convolutional Networks using Vector Quantization. arXiv:cs.CV/1412.6115 Y. Gong et al. 2014. Compressing Deep Convolutional Networks using Vector Quantization. arXiv:cs.CV/1412.6115

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