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20220226_The_Case_for_In-Memory_Inferencing_of_Quantized_CNNs_at_the_Edge.pdf (2.29 MB)
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The Case for In-Memory Inferencing of Quantized CNNs at the Edge

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posted on 07.03.2022, 03:04 authored by Gabriel Falcao, João Dinis FerreiraJoão Dinis Ferreira
As artificial intelligence becomes a pervasive tool for the billions of IoT devices at the edge, the data movement bottleneck imposes severe limitations on these systems’ performance and autonomy. Processing-in-Memory emerges as a way to mitigate the data movement bottleneck while satisfying the stringent performance, energy efficiency, and accuracy requirements of edge imaging applications that rely on convolutional neural networks.

Funding

Hardware Accelerated Deep LEarning framework

Fundação para a Ciência e Tecnologia

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EXPL/EEI-HAC/1511/2021

UIDB/EEA/50008/2020

History

Email Address of Submitting Author

gff@deec.uc.pt

ORCID of Submitting Author

0000-0001-9805-6747

Submitting Author's Institution

Instituto de Telecomunicações, Universidade de Coimbra

Submitting Author's Country

Portugal