A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware
Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders t...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:6e4eedc885474c579ffec00f6a476c8a2021-11-16T07:39:16ZA Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware1662-453X10.3389/fnins.2021.694170https://doaj.org/article/6e4eedc885474c579ffec00f6a476c8a2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.694170/fullhttps://doaj.org/toc/1662-453XArtificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders their deployment on mobile and embedded systems. Highly inspired from biological brain, spiking neural networks (SNNs) are emerging as new solutions because of natural superiority in brain-like learning and great energy efficiency with event-driven communication and computation. Nevertheless, training a deep SNN remains a main challenge and there is usually a big accuracy gap between ANNs and SNNs. In this paper, we introduce a hardware-friendly conversion algorithm called “scatter-and-gather” to convert quantized ANNs to lossless SNNs, where neurons are connected with ternary {−1,0,1} synaptic weights. Each spiking neuron is stateless and more like original McCulloch and Pitts model, because it fires at most one spike and need be reset at each time step. Furthermore, we develop an incremental mapping framework to demonstrate efficient network deployments on a reconfigurable neuromorphic chip. Experimental results show our spiking LeNet on MNIST and VGG-Net on CIFAR-10 datasetobtain 99.37% and 91.91% classification accuracy, respectively. Besides, the presented mapping algorithm manages network deployment on our neuromorphic chip with maximum resource efficiency and excellent flexibility. Our four-spike LeNet and VGG-Net on chip can achieve respective real-time inference speed of 0.38 ms/image, 3.24 ms/image, and an average power consumption of 0.28 mJ/image and 2.3 mJ/image at 0.9 V, 252 MHz, which is nearly two orders of magnitude more efficient than traditional GPUs.Chenglong ZouChenglong ZouXiaoxin CuiYisong KuangKefei LiuYuan WangXinan WangRu HuangFrontiers Media S.A.articleconvolutional neural networkspiking neural networknetwork quantizationnetwork conversionneuromorphic hardwarenetwork mappingNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
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EN |
| topic |
convolutional neural network spiking neural network network quantization network conversion neuromorphic hardware network mapping Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
| spellingShingle |
convolutional neural network spiking neural network network quantization network conversion neuromorphic hardware network mapping Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Chenglong Zou Chenglong Zou Xiaoxin Cui Yisong Kuang Kefei Liu Yuan Wang Xinan Wang Ru Huang A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware |
| description |
Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders their deployment on mobile and embedded systems. Highly inspired from biological brain, spiking neural networks (SNNs) are emerging as new solutions because of natural superiority in brain-like learning and great energy efficiency with event-driven communication and computation. Nevertheless, training a deep SNN remains a main challenge and there is usually a big accuracy gap between ANNs and SNNs. In this paper, we introduce a hardware-friendly conversion algorithm called “scatter-and-gather” to convert quantized ANNs to lossless SNNs, where neurons are connected with ternary {−1,0,1} synaptic weights. Each spiking neuron is stateless and more like original McCulloch and Pitts model, because it fires at most one spike and need be reset at each time step. Furthermore, we develop an incremental mapping framework to demonstrate efficient network deployments on a reconfigurable neuromorphic chip. Experimental results show our spiking LeNet on MNIST and VGG-Net on CIFAR-10 datasetobtain 99.37% and 91.91% classification accuracy, respectively. Besides, the presented mapping algorithm manages network deployment on our neuromorphic chip with maximum resource efficiency and excellent flexibility. Our four-spike LeNet and VGG-Net on chip can achieve respective real-time inference speed of 0.38 ms/image, 3.24 ms/image, and an average power consumption of 0.28 mJ/image and 2.3 mJ/image at 0.9 V, 252 MHz, which is nearly two orders of magnitude more efficient than traditional GPUs. |
| format |
article |
| author |
Chenglong Zou Chenglong Zou Xiaoxin Cui Yisong Kuang Kefei Liu Yuan Wang Xinan Wang Ru Huang |
| author_facet |
Chenglong Zou Chenglong Zou Xiaoxin Cui Yisong Kuang Kefei Liu Yuan Wang Xinan Wang Ru Huang |
| author_sort |
Chenglong Zou |
| title |
A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware |
| title_short |
A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware |
| title_full |
A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware |
| title_fullStr |
A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware |
| title_full_unstemmed |
A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware |
| title_sort |
scatter-and-gather spiking convolutional neural network on a reconfigurable neuromorphic hardware |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/6e4eedc885474c579ffec00f6a476c8a |
| work_keys_str_mv |
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