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Lightening the Load with Highly Accurate Storage- and Energy-Efficient LightNNs by Ruizhou Ding; Zeye Liu; R. D. (Shawn) Blanton; Diana Marculescu is a Computer Science article available to read on EtoBox.

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Hardware implementations of deep neural networks (DNNs) have been adopted in many systems because of their higher classification speed. However, while they may be characterized by better accuracy, larger DNNs require significant energy and area, thereby limiting their wide adoption. The energy consumption of DNNs is driven by both memory accesses and computation. Binarized neural networks (BNNs), as a tradeoff between accuracy and energy consumption, can achieve great energy reduction and have good accuracy for large DNNs due to their regularization effect. However, BNNs show poor accuracy when a smaller DNN configuration is adopted. In this article, we propose a new DNN architecture, LightNN, which replaces the multiplications to one shift or a constrained number of shifts and adds. Our theoretical analysis for LightNNs shows that their accuracy is maintained while dramatically reducing storage and energy requirements. For a fixed DNN configuration, LightNNs have better accuracy at a slight energy increase than BNNs, yet are more energy efficient with only slightly less accuracy than conventional DNNs. Therefore, LightNNs provide more options for hardware designers to trade off ac

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Author

Ruizhou Ding; Zeye Liu; R. D. (Shawn) Blanton; Diana Marculescu

Publisher

ACM

Published

2018

Language

EN

Field

Computer Science (Physical Sciences)