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Article

Customizable Vector Acceleration in Extreme-Edge Computing: A RISC-V Software/Hardware Architecture Study on VGG-16 Implementation

Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00185 Rome, Italy
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Author to whom correspondence should be addressed.
Academic Editor: Luis Gomes
Electronics 2021, 10(4), 518; https://doi.org/10.3390/electronics10040518
Received: 26 January 2021 / Revised: 14 February 2021 / Accepted: 19 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue Advanced Embedded HW/SW Development)
Computing in the cloud-edge continuum, as opposed to cloud computing, relies on high performance processing on the extreme edge of the Internet of Things (IoT) hierarchy. Hardware acceleration is a mandatory solution to achieve the performance requirements, yet it can be tightly tied to particular computation kernels, even within the same application. Vector-oriented hardware acceleration has gained renewed interest to support artificial intelligence (AI) applications like convolutional networks or classification algorithms. We present a comprehensive investigation of the performance and power efficiency achievable by configurable vector acceleration subsystems, obtaining evidence of both the high potential of the proposed microarchitecture and the advantage of hardware customization in total transparency to the software program. View Full-Text
Keywords: edge-computing; processors; hardware acceleration edge-computing; processors; hardware acceleration
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MDPI and ACS Style

Sordillo, S.; Cheikh, A.; Mastrandrea, A.; Menichelli, F.; Olivieri, M. Customizable Vector Acceleration in Extreme-Edge Computing: A RISC-V Software/Hardware Architecture Study on VGG-16 Implementation. Electronics 2021, 10, 518. https://doi.org/10.3390/electronics10040518

AMA Style

Sordillo S, Cheikh A, Mastrandrea A, Menichelli F, Olivieri M. Customizable Vector Acceleration in Extreme-Edge Computing: A RISC-V Software/Hardware Architecture Study on VGG-16 Implementation. Electronics. 2021; 10(4):518. https://doi.org/10.3390/electronics10040518

Chicago/Turabian Style

Sordillo, Stefano, Abdallah Cheikh, Antonio Mastrandrea, Francesco Menichelli, and Mauro Olivieri. 2021. "Customizable Vector Acceleration in Extreme-Edge Computing: A RISC-V Software/Hardware Architecture Study on VGG-16 Implementation" Electronics 10, no. 4: 518. https://doi.org/10.3390/electronics10040518

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