Error Resilient In-Memory Computing Architecture for CNN Inference on the Edge-Reference-Cited by-同舟云学术

Error Resilient In-Memory Computing Architecture for CNN Inference on the Edge

Published:2022-06-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Great Lakes Symposium on VLSI 2022
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Author:

Rios Marco¹,Ponzina Flavio¹,Ansaloni Giovanni¹,Levisse Alexandre¹,Atienza David¹

Affiliation:

1. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

Funder

ERC Consolidator Grant COMPUSAPIEN

EC H2020 FVLLMONTI

Swiss NSF ML-Edge RTD

EC H2020 WiPLASH

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3526241.3530351

Reference27 articles.

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2. M. Amin-Naji et al . 2019 . Ensemble of CNN for multi-focus image fusion. Elsevier , Information Fusion. M. Amin-Naji et al. 2019. Ensemble of CNN for multi-focus image fusion. Elsevier, Information Fusion.

3. D. Bortolotti etal 2014. Approximate compressed sensing: Ultra-low power biosignal processing via aggressive voltage scaling on a hybrid memory multi-core processor. ISLPED. D. Bortolotti et al. 2014. Approximate compressed sensing: Ultra-low power biosignal processing via aggressive voltage scaling on a hybrid memory multi-core processor. ISLPED.

4. G. Burr etal 2015. Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses) using phase-change memory as the synaptic weight element. IEEE TED. G. Burr et al. 2015. Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses) using phase-change memory as the synaptic weight element. IEEE TED.

5. Eyeriss

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