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Efficient Spiking Neural Network Training and Inference with Reduced Precision Memory and Computing
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- Authors
- Yi Wang, Karim Shahbazi, Hao Zhang, Kwang-Il Oh, Jae-Jin Lee, Seok-Bum Ko
- Issue Date
- 2019-09
- Citation
- IET Computers and Digital Techniques, v.13, no.5, pp.397-404
- ISSN
- 1751-8601
- Publisher
- IET
- Language
- English
- Type
- Journal Article
- Abstract
- In this study, reduced precision operations are investigated in order to improve the speed and energy efficiency of SNN implementation. Instead of using the 32-bit single-precision floating-point format, small floating-point format and fixed-point format are used to represent SNN parameters and to perform SNN operations. The analyses are performed on the training and inference of a leaky integrate-and-fire model-based SNN that is trained and used to classify the handwritten digits in MNIST database. The analysis results show that for SNN inference, the floating-point format with 4-bit exponent and 3-bit mantissa or the fixed-point format with 6-bit integer and 7-bit fraction can be used without any accuracy degradation. For training, a floating-point format with 5-bit exponent and 3-bit mantissa or a fixed-point format with 6-bit integer and 10-bit fraction can be used to obtain full accuracy. The proposed reduced precision formats can be used in SNN hardware accelerator design and the selection between floating-point and fixed-point can be determined by design requirements. A case study of SNN implementation on field-programmable gate array device is performed. With reduced precision numerical formats, memory footprint, computing speed, and resource utilisation are improved. As a result, the energy efficiency of SNN implementation is also improved.
- KSP Keywords
- Case studies, Design requirements, Energy efficiency, Field-Programmable Gate Array(FPGA), Handwritten digits, Hardware accelerator, Integrate-and-fire model, MNIST database, Single-precision, accelerator design, fixed point