In:
Japanese Journal of Applied Physics, IOP Publishing, Vol. 60, No. SB ( 2021-05-01), p. SBBB07-
Kurzfassung:
This paper presents an energy-efficient hardware accelerator for binarized convolutional neural networks (BCNNs). In this BCNN accelerator, a data-shift operation becomes dominant to effectively control input/weight-data streams under limited memory bandwidth. A magnetic-tunnel-junction (MTJ)-based nonvolatile field-programmable gate array (NV-FPGA), where the amount of stored-data updating is minimized in a configurable logic block, is a well-suited hardware platform for implementing such a BCNN accelerator. Owing to the nonvolatile storage capability of the NV-FPGA, not only power consumption in the data-shift operation but also standby power consumption in the idle function block is reduced without losing internal data. It is demonstrated under 45 nm complementary metal–oxide–semiconductor/MTJ process technologies that the energy consumption of the proposed BCNN accelerator is 50.7% lower than that of a BCNN accelerator using a conventional static-random-access-memory-based FPGA.
Materialart:
Online-Ressource
ISSN:
0021-4922
,
1347-4065
DOI:
10.35848/1347-4065/abe682
Sprache:
Unbekannt
Verlag:
IOP Publishing
Publikationsdatum:
2021
ZDB Id:
218223-3
ZDB Id:
797294-5
ZDB Id:
2006801-3
ZDB Id:
797295-7
