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ΔnN: Power-Efficient Neural Network Acceleration Using Differential Weights
Univ Tehran, Dept Elect & Comp Engn, Comp Engn, Tehran, Iran..
Univ Tehran, Tehran, Iran..
Univ Tehran, Dept Elect & Comp Engn, Comp Engn, Tehran, Iran..
Univ Tehran, Coll Engn, Dept Elect & Comp Engn, Tehran, Iran..
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2020 (English)In: IEEE Micro, ISSN 0272-1732, E-ISSN 1937-4143, Vol. 40, no 1, p. 67-74Article in journal (Refereed) Published
Abstract [en]

The enormous and ever-increasing complexity of state-of-the-art neural networks has impeded the deployment of deep learning on resource-limited embedded and mobile devices. To reduce the complexity of neural networks, this article presents Delta NN, a power-efficient architecture that leverages a combination of the approximate value locality of neuron weights and algorithmic structure of neural networks. Delta NN keeps each weight as its difference (Delta) to the nearest smaller weight: each weight reuses the calculations of the smaller weight, followed by a calculation on the Delta value to make up the difference. We also round up/down the Delta to the closest power of two numbers to further reduce complexity. The experimental results show that Delta NN boosts the average performance by 14%-37% and reduces the average power consumption by 17%-49% over some state-of-the-art neural network designs.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC , 2020. Vol. 40, no 1, p. 67-74
National Category
Communication Systems
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URN: urn:nbn:se:mdh:diva-47029DOI: 10.1109/MM.2019.2948345ISI: 000508573000010Scopus ID: 2-s2.0-85073748116OAI: oai:DiVA.org:mdh-47029DiVA, id: diva2:1392885
Available from: 2020-02-13 Created: 2020-02-13 Last updated: 2020-02-20Bibliographically approved

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