Approximate Computing for Machine Learning Workloads: A Circuits and Systems Perspective

Author:

Sanyal Sourav,Negi Shubham,Raghunathan Anand,Roy Kaushik

Publisher

Springer International Publishing

Reference29 articles.

1. Liu W, Lombardi F, Schulte M. Approximate computing: from circuits to applications. Proc IEEE. 2020;108(12):2103–7

2. Esmaeilzadeh H, Blem E, Amant RS, Sankaralingam K, Burger D. Dark silicon and the end of multicore scaling. In: 2011 38th annual international symposium on computer architecture (ISCA), 2011 Jun 4. New York: IEEE; 2011, pp 365–376

3. Venkataramani S, Chakradhar ST, Roy K, Raghunathan A. Approximate computing and the quest for computing efficiency. In: Proceedings of the 52nd Annu. Des. Automat. Conf., San Francisco, CA, Jun 2015; pp. 120-1–120-6

4. Chippa VK et al. Analysis and characterization of inherent application resilience for approximate computing. In Proceedings of the DAC; 2013

5. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neur Inf Process Syst 2012;25:1097–105

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