Performance Evaluation of Feed-Forward Backpropagation Neural Network for Classification on a Reconfigurable Hardware Architecture
| Autor: | Sanjay S. Singapuram, Rohit Ronge, S. K. Nandy, Mahnaz Mohammadi |
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| Rok vydání: | 2016 |
| Předmět: |
Hardware architecture
Virtex Speedup Computer architecture Artificial neural network Computer science Scalability 0202 electrical engineering electronic engineering information engineering Hardware acceleration 020201 artificial intelligence & image processing 02 engineering and technology Field-programmable gate array Backpropagation |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319304809 ARC |
| DOI: | 10.1007/978-3-319-30481-6_25 |
| Popis: | Performance of classification using Feed-Forward Backpropagation Neural Network FFBPNN on a reconfigurable hardware architecture is evaluated in this paper. The hardware architecture used for implementation of FFBPNN in this paper is a set of interconnected HyperCells which serve as reconfigurable data paths for the network. The architecture is easily scalable and able to implement networks with no limitation on their number of input and output dimensions. The performance of FFBPNN implemented on network of HCs using Xilinx Virtex 7 XC7V2000T as target FPGA is compared with software implementation and GPU implementation of FFBPNN. Results show speed up of 1.02X-3.49X over equivalent software implementation on Intel Core 2 Quad and 1.07X-6X over GPU NVIDIA GTX650. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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