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Measuring the Energy Consumption and Efficiency of Deep Neural Networks:
  An Empirical Analysis and Design Recommendations

Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

13 March 2024
Charles Edison Tripp
J. Perr-Sauer
Jamil Gafur
Amabarish Nag
Avi Purkayastha
S. Zisman
Erik A. Bensen
ArXivPDFHTML

Papers citing "Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations"

2 / 2 papers shown
Title
NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealities
NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealities
James Read
Ming-Yen Lee
Wei-Hsing Huang
Yuan-Chun Luo
A. Lu
Shimeng Yu
32
0
0
05 May 2025
A Transistor Operations Model for Deep Learning Energy Consumption
  Scaling Law
A Transistor Operations Model for Deep Learning Energy Consumption Scaling Law
Chen Li
Antonios Tsourdos
Weisi Guo
AI4CE
20
1
0
30 May 2022
1