Trading-off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IEEE TCAD), 2018

29 January 2019

Nitthilan Kanappan Jayakodi

Papers citing "Trading-off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach"

7 / 7 papers shown

Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting

Matthew Blaschko

220

21 May 2024

Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise

Krishnendu Chakrabarty

Hai Helen Li

AAML

12 Sep 2021

DynO: Dynamic Onloading of Deep Neural Networks from Cloud to DeviceACM Transactions on Embedded Computing Systems (TECS), 2021

Mario Almeida

Stefanos Laskaridis

Stylianos I. Venieris

Ilias Leontiadis

Nicholas D. Lane

192

20 Apr 2021

Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-designIEEE design & test (DT), 2021

Cong Hao

Jordan Dotzel

Jinjun Xiong

Luca Benini

Zhiru Zhang

Deming Chen

197

25 Mar 2021

SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms

Nitthilan Kanappan Jayakodi

J. Doppa

P. Pande

GAN

144

23 Mar 2021

Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge DevicesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020

183

30 Oct 2020

Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning

180

07 Jul 2020