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A survey of FPGA-based accelerators for convolutional neural networks
- Source :
- Neural Computing and Applications. 32:1109-1139
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of cognitive tasks, and due to this, they have received significant interest from the researchers. Given the high computational demands of CNNs, custom hardware accelerators are vital for boosting their performance. The high energy efficiency, computing capabilities and reconfigurability of FPGA make it a promising platform for hardware acceleration of CNNs. In this paper, we present a survey of techniques for implementing and optimizing CNN algorithms on FPGA. We organize the works in several categories to bring out their similarities and differences. This paper is expected to be useful for researchers in the area of artificial intelligence, hardware architecture and system design.
- Subjects :
- Hardware architecture
0209 industrial biotechnology
Artificial neural network
Computer science
business.industry
Deep learning
Reconfigurability
02 engineering and technology
Convolutional neural network
020901 industrial engineering & automation
Computer architecture
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Systems design
Hardware acceleration
020201 artificial intelligence & image processing
Artificial intelligence
Field-programmable gate array
business
Software
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 32
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........054bae9b11064abd4ec5f16454a258dc