Early-Stage Neural Network Hardware Performance Analysis

Alex Karbachevsky, Chaim Baskin, Evgenii Zheltonozhskii, Yevgeny Yermolin, Freddy Gabbay, Alex M. Bronstein and Avi Mendelson
Additional contact information
Alex Karbachevsky: Technion—Israel Institute of Technology, Haifa 3200003, Israel
Chaim Baskin: Technion—Israel Institute of Technology, Haifa 3200003, Israel
Evgenii Zheltonozhskii: Technion—Israel Institute of Technology, Haifa 3200003, Israel
Yevgeny Yermolin: Technion—Israel Institute of Technology, Haifa 3200003, Israel
Freddy Gabbay: Ruppin Academic Center, Emek Hefer 4025000, Israel
Alex M. Bronstein: Technion—Israel Institute of Technology, Haifa 3200003, Israel
Avi Mendelson: Technion—Israel Institute of Technology, Haifa 3200003, Israel

Sustainability, 2021, vol. 13, issue 2, 1-20

Abstract: 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.

Keywords: neural networks; accelerators; quantization; CNN architecture (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/2/717/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/2/717/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:2:p:717-:d:479906

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().