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Title: Demonstrating Reduced-Voltage FPGA-Based Neural Network Acceleration for Power-Efficiency
Authors: Onural, E. B.
Yüksel, I. E.
Salami, B.
Keywords: DNN
Power efficiency
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 30th International Conference on Field-Programmable Logic and Applications, FPL 2020, 31 August 2020 through 4 September 2020, , 163966
Abstract: This demo aims to demonstrate undervolting below the nominal level set by the vendor for off-the-shelf FPGAs running Deep Neural Networks (DNNs), to achieve power-efficiency. FPGAs are becoming popular [1-4], thanks to their higher throughput than GPUs and better flexibility than ASICs. To further improve the power-efficiency, we propose to employ undervolting below the nominal level (i.e., V_nom= 850mV for studied platform). FPGA vendors usually add a voltage guardband to ensure the correct operation under the worst-case circuit and environmental conditions [5-9]. However, these guardbands can be very conservative and unnecessary for state-of-the-art applications. Reducing the voltage in this guardband region does not lead to reliability issues under normal operating conditions, and thus, eliminating it can result in a significant power reduction for a wide variety of real-world applications. We will experimentally demonstrate a large voltage guardband for modern FPGAs: an average of 33%. Eliminating this guardband leads to significant power-efficiency (GOPs/W) improvement, on average, 2.6X, see Figure 1. © 2020 IEEE.
ISBN: 9781728199023
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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