ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2011.11840
  4. Cited By
Benchmarking Inference Performance of Deep Learning Models on Analog
  Devices
v1v2 (latest)

Benchmarking Inference Performance of Deep Learning Models on Analog Devices

IEEE International Joint Conference on Neural Network (IJCNN), 2020
24 November 2020
Omobayode Fagbohungbe
Lijun Qian
ArXiv (abs)PDFHTML

Papers citing "Benchmarking Inference Performance of Deep Learning Models on Analog Devices"

7 / 7 papers shown
Otters: An Energy-Efficient SpikingTransformer via Optical Time-to-First-Spike Encoding
Otters: An Energy-Efficient SpikingTransformer via Optical Time-to-First-Spike Encoding
Zhanglu Yan
Jiayi Mao
Qianhui Liu
Fanfan Li
Gang Pan
Tao Luo
Bowen Zhu
Weng-Fai Wong
181
3
0
23 Sep 2025
Effect of Batch Normalization on Noise Resistant Property of Deep
  Learning Models
Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models
Omobayode Fagbohungbe
Lijun Qian
246
15
0
15 May 2022
Impact of Learning Rate on Noise Resistant Property of Deep Learning
  Models
Impact of Learning Rate on Noise Resistant Property of Deep Learning Models
Omobayode Fagbohungbe
Lijun Qian
215
5
0
08 May 2022
Impact of L1 Batch Normalization on Analog Noise Resistant Property of
  Deep Learning Models
Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning ModelsIEEE International Joint Conference on Neural Network (IJCNN), 2022
Omobayode Fagbohungbe
Lijun Qian
205
1
0
07 May 2022
Exploring the Impact of Virtualization on the Usability of the Deep
  Learning Applications
Exploring the Impact of Virtualization on the Usability of the Deep Learning Applications
Davood Ghatreh Samani
M. Salehi
162
9
0
17 Dec 2021
Denoising Noisy Neural Networks: A Bayesian Approach with Compensation
Denoising Noisy Neural Networks: A Bayesian Approach with CompensationIEEE Transactions on Signal Processing (IEEE TSP), 2021
Yulin Shao
Soung Chang Liew
Deniz Gunduz
437
16
0
22 May 2021
Neural Network Compression for Noisy Storage Devices
Neural Network Compression for Noisy Storage DevicesACM Transactions on Embedded Computing Systems (TECS), 2021
Berivan Isik
Kristy Choi
Xin-Yang Zheng
Tsachy Weissman
Stefano Ermon
H. P. Wong
Armin Alaghi
394
13
0
15 Feb 2021
1
Page 1 of 1