abstract

• One of the great challenges of computing today is sustainable energy consumption. In the deployment of edge computing this challenge is particularly important considering the use of embedded equipment with limited energy and computation resources. In those systems, the energy consumption must be carefully managed to operate for long periods. Specifically, for embedded systems with machine learning capabilities in the Internet of Things (EMLIoT) era, the convolutional neural networks (CNN) model execution is energy challenging and requires massive data. Nowadays, high workload processing is designed separately into a host processor in charge of generic functions and an accelerator dedicated to executing the specific task. Open-hardware-based designs are pushing for new levels of energy efficiency. For achieving energy efficiency, open-source tools, such as the RISC-V ISA, have been introduced to optimize every internal stage of the system. This document aims to compare the EMLIoT accelerator designs based on RISC-V and highlights open topics for research. © 2022 IEEE.

publication date

• 2022-01-01

funding provided via

• F-PROMEP-01/Rev-04 SEP-23-002-A, contractPID2019-107255GB-C21/AEI/10.13039/501100011033; Generalitat de Catalunya: 2017-SGR-1328  Grant

published in

• 2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022  Proceedings

keywords

• accelerator; energy efficiency; RISC-V Convolutional neural networks; Embedded systems; Energy utilization; Green computing; Open systems; Computation resources; Convolutional neural network; Edge computing; Embedded equipments; Embedded-system; Energy-consumption; Limited energy resource; Machine-learning; RISC-V; Sustainable energy; Energy efficiency

Digital Object Identifier (DOI)

• 10.1109/COINS54846.2022.9855006

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