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Open AccessArticle

Early-Stage Neural Network Hardware Performance Analysis

1
Technion—Israel Institute of Technology, Haifa 3200003, Israel
2
Ruppin Academic Center, Emek Hefer 4025000, Israel
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sustainability 2021, 13(2), 717; https://doi.org/10.3390/su13020717
Received: 26 November 2020 / Revised: 27 December 2020 / Accepted: 10 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue Energy-Efficient Computing Systems for Deep Learning)
The demand for running NNs in embedded environments has increased significantly in recent years due to the significant success of convolutional neural network (CNN) approaches in various tasks, including image recognition and generation. The task of achieving high accuracy on resource-restricted devices, however, is still considered to be challenging, which is mainly due to the vast number of design parameters that need to be balanced. While the quantization of CNN parameters leads to a reduction of power and area, it can also generate unexpected changes in the balance between communication and computation. This change is hard to evaluate, and the lack of balance may lead to lower utilization of either memory bandwidth or computational resources, thereby reducing performance. This paper introduces a hardware performance analysis framework for identifying bottlenecks in the early stages of CNN hardware design. We demonstrate how the proposed method can help in evaluating different architecture alternatives of resource-restricted CNN accelerators (e.g., part of real-time embedded systems) early in design stages and, thus, prevent making design mistakes. View Full-Text
Keywords: neural networks; accelerators; quantization; CNN architecture neural networks; accelerators; quantization; CNN architecture
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MDPI and ACS Style

Karbachevsky, A.; Baskin, C.; Zheltonozhskii, E.; Yermolin, Y.; Gabbay, F.; Bronstein, A.M.; Mendelson, A. Early-Stage Neural Network Hardware Performance Analysis. Sustainability 2021, 13, 717. https://doi.org/10.3390/su13020717

AMA Style

Karbachevsky A, Baskin C, Zheltonozhskii E, Yermolin Y, Gabbay F, Bronstein AM, Mendelson A. Early-Stage Neural Network Hardware Performance Analysis. Sustainability. 2021; 13(2):717. https://doi.org/10.3390/su13020717

Chicago/Turabian Style

Karbachevsky, Alex; Baskin, Chaim; Zheltonozhskii, Evgenii; Yermolin, Yevgeny; Gabbay, Freddy; Bronstein, Alex M.; Mendelson, Avi. 2021. "Early-Stage Neural Network Hardware Performance Analysis" Sustainability 13, no. 2: 717. https://doi.org/10.3390/su13020717

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