Performance of deep neural networks on low-power IoT devices
Paper i proceeding, 2021

Advances in deep learning have revolutionized machine learning by solving complex tasks such as image, speech, and text recognition. However, training and inference of deep neural networks are resource-intensive. Recently, researchers made efforts to bring inference to IoT edge and sensor devices which have become the prime data sources nowadays. However, running deep neural networks on low-power IoT devices is challenging due to their resource-constraints in memory, compute power, and energy. This paper presents a benchmark to grasp these trade-offs by evaluating three representative deep learning frameworks: uTensor, TF-Lite-Micro, and CMSIS-NN. Our benchmark reveals significant differences and trade-offs for each framework and its tool-chain: (1) We find that uTensor is the most straightforward framework to use, followed by TF-Micro, and then CMSIS-NN. (2) Our evaluation shows large differences in energy, RAM, and Flash footprints. For example, in terms of energy, CMSIS-NN is the most efficient, followed by TF-Micro and then uTensor, each with a significant gap.

Deep Neural Networks

Low-Power

IoT

Författare

Christos Profentzas

Chalmers, Data- och informationsteknik, Nätverk och system

Magnus Almgren

Chalmers, Data- och informationsteknik, Nätverk och system

Olaf Landsiedel

Christian-Albrechts-Universität zu Kiel

CPS-IoTBench '21: Proceedings of the Workshop on Benchmarking Cyber-Physical Systems and Internet of Things
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Ämneskategorier

Datorteknik

Datavetenskap (datalogi)

Datorsystem

Styrkeområden

Informations- och kommunikationsteknik

Drivkrafter

Hållbar utveckling

DOI

10.1145/3458473.3458823

Mer information

Senast uppdaterat

2021-05-20