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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.8 No.5
- 발행연도
- 2019.10
- 수록면
- 405 - 414 (10page)
- DOI
- 10.5573/IEIESPC.2019.8.5.405
이용수
초록· 키워드
오류제보하기
Artificial neural networks (ANNs) are being studied in various fields. However, classic ANNs have limitations in hardware implementation, due to computational complexity. On the other hand, spiking neural networks (SNNs), which are inspired by biological neural systems, have optimal characteristics in hardware implementation. In the SNN, communication is performed between neurons by using spikes, which are represented by a single bit. This reduces computational complexity and logic occupation in a device. SNNs have weights and delays as adjustable parameters, and have been successfully used for image classification. Although there are several mathematical spiking neuron models, to reduce computational complexity, this paper proposes a simplified and digital leaky integrate-and-fire (SDLIF) model, which is computationally efficient and powerful. Temporal coding is used as neural coding. We also describe a field-programmable gate array (FPGA) implementation using Verilog hardware description language (HDL), and discuss simple image pattern classification problems as verification. The final results demonstrate not only the performance of the SNN for image pattern recognition and classification, but also its efficiency, such as low logic occupation in the device, and low power consumption on the FPGA.
목차
Abstract
1. Introduction
2. Spiking Neuron Models
3. Simplified Spiking Neuron Model and Interconnection
4. Hardware Implementation
5. Experimental Results
6. Conclusions
References
참고문헌 (30)
참고문헌 신청
Cireşan, D / 2012 / Multicolumn deep neural networks for image classification
Jarrett, K / 2009 / What is the best multi-stage architecture for object recognition? / Proceedings of the 2009 IEEE 12th International Conference on Computer Vision (ICCV) :2146~2153
Hinton, G / 2012 / Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups / IEEE Signal Processing Magazine 29:82~97
Amato, F / 2013 / Artificial neural networks in medical diagnosis / Journal of Applied Biomedicine 11:47~58
Hee-Su, K / 2016 / Low Power Implementation of Spiking Deep Neural Network / Proceedings of the 2016 IEIE Fall Conference :758~761
Jarrett, K / 2009 / What is the best multi-stage architecture for object recognition? / Proceedings of the 2009 IEEE 12th International Conference on Computer Vision (ICCV) :2146~2153
Hinton, G / 2012 / Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups / IEEE Signal Processing Magazine 29:82~97
Amato, F / 2013 / Artificial neural networks in medical diagnosis / Journal of Applied Biomedicine 11:47~58
Hee-Su, K / 2016 / Low Power Implementation of Spiking Deep Neural Network / Proceedings of the 2016 IEIE Fall Conference :758~761
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