---
OA_place: publisher
OA_type: gold
_id: '18238'
abstract:
- lang: eng
  text: 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.
article_number: '717'
article_processing_charge: No
article_type: original
author:
- first_name: Alex
  full_name: Karbachevsky, Alex
  last_name: Karbachevsky
- first_name: Chaim
  full_name: Baskin, Chaim
  last_name: Baskin
- first_name: Evgenii
  full_name: Zheltonozhskii, Evgenii
  last_name: Zheltonozhskii
- first_name: Yevgeny
  full_name: Yermolin, Yevgeny
  last_name: Yermolin
- first_name: Freddy
  full_name: Gabbay, Freddy
  last_name: Gabbay
- first_name: Alexander
  full_name: Bronstein, Alexander
  id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
  last_name: Bronstein
  orcid: 0000-0001-9699-8730
- first_name: Avi
  full_name: Mendelson, Avi
  last_name: Mendelson
citation:
  ama: Karbachevsky A, Baskin C, Zheltonozhskii E, et al. Early-stage neural network
    hardware performance analysis. <i>Sustainability</i>. 2021;13(2). doi:<a href="https://doi.org/10.3390/su13020717">10.3390/su13020717</a>
  apa: Karbachevsky, A., Baskin, C., Zheltonozhskii, E., Yermolin, Y., Gabbay, F.,
    Bronstein, A. M., &#38; Mendelson, A. (2021). Early-stage neural network hardware
    performance analysis. <i>Sustainability</i>. MDPI. <a href="https://doi.org/10.3390/su13020717">https://doi.org/10.3390/su13020717</a>
  chicago: Karbachevsky, Alex, Chaim Baskin, Evgenii Zheltonozhskii, Yevgeny Yermolin,
    Freddy Gabbay, Alex M. Bronstein, and Avi Mendelson. “Early-Stage Neural Network
    Hardware Performance Analysis.” <i>Sustainability</i>. MDPI, 2021. <a href="https://doi.org/10.3390/su13020717">https://doi.org/10.3390/su13020717</a>.
  ieee: A. Karbachevsky <i>et al.</i>, “Early-stage neural network hardware performance
    analysis,” <i>Sustainability</i>, vol. 13, no. 2. MDPI, 2021.
  ista: Karbachevsky A, Baskin C, Zheltonozhskii E, Yermolin Y, Gabbay F, Bronstein
    AM, Mendelson A. 2021. Early-stage neural network hardware performance analysis.
    Sustainability. 13(2), 717.
  mla: Karbachevsky, Alex, et al. “Early-Stage Neural Network Hardware Performance
    Analysis.” <i>Sustainability</i>, vol. 13, no. 2, 717, MDPI, 2021, doi:<a href="https://doi.org/10.3390/su13020717">10.3390/su13020717</a>.
  short: A. Karbachevsky, C. Baskin, E. Zheltonozhskii, Y. Yermolin, F. Gabbay, A.M.
    Bronstein, A. Mendelson, Sustainability 13 (2021).
date_created: 2024-10-08T12:58:47Z
date_published: 2021-01-13T00:00:00Z
date_updated: 2024-10-15T08:17:49Z
day: '13'
doi: 10.3390/su13020717
extern: '1'
intvolume: '        13'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.3390/su13020717
month: '01'
oa: 1
oa_version: Published Version
publication: Sustainability
publication_identifier:
  issn:
  - 2071-1050
publication_status: published
publisher: MDPI
quality_controlled: '1'
scopus_import: '1'
status: public
title: Early-stage neural network hardware performance analysis
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2021'
...