Accelerating Local Binary Pattern Networks with Software-Programmable FPGAs
| Autor: | Jeng-Hau Lin, Atieh Lotfi, Rajesh Gupta, Vahideh Akhlaghi, Zhuowen Tu |
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| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Boosting (machine learning) Artificial neural network business.industry Computer science Quantization (signal processing) Feature extraction 02 engineering and technology 01 natural sciences Convolutional neural network 020202 computer hardware & architecture High memory Software Computer architecture 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Field-programmable gate array business |
| Zdroj: | DATE |
| Popis: | Fueled by the success of mobile devices, the computational demands on these platforms have been rising faster than the computational and storage capacities or energy availability to perform tasks ranging from recognizing speech, images to automated reasoning and cognition. While the success of convolutional neural networks (CNNs) have contributed to such a vision, these algorithms stay out of the reach of limited computing and storage capabilities of mobile platforms. It is clear to most researchers that such a transition can only be achieved by using dedicated hardware accelerators on these platforms. However, CNNs with arithmetic-intensive operations remain particularly unsuitable for such acceleration both computationally as well as for the high memory bandwidth needs of highly parallel processing required. In this paper, we implement and optimize an alternative genre of networks, local binary pattern network (LBPNet) which eliminates arithmetic operations by combinatorial operations thus substantially boosting the efficiency of hardware implementation. LBPNet is built upon a radically different view of the arithmetic operations sought by conventional neural networks to overcome limitations posed by compression and quantization methods used for hardware implementation of CNNs. This paper explores in depth the design and implementation of both an architecture and critical optimizations of LBPNet for realization in accelerator hardware and provides a comparison of results with the state-of-art CNN on multiple datasets. |
| Databáze: | OpenAIRE |
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