.png){kind=icon}
返回
Batch normalization-free weight-binarized SNN based on hardware-saving IF neuron
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2023.126234.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Neuromorphic computing realizes low-latency and low-power computing by emulating the neural struc-ture and operation of the human brain, and is considered a key research area for third-generation artifi-cial intelligence. However, current neuromorphic computing faces the problems of huge synaptic memory consumption and complex neuron calculations. This paper proposes a batch normalization (BN)-free weight-binarized SNN based on hardware-saving IF neurons to reduce storage requirements and improve the computational efficiency of neuromorphic computing. Hardware-friendly backpropaga-tion through time (BPTT)-based algorithm and SG function are proposed to calculate the gradients of the integrate process and fire process of IF neuron, respectively. Weight binarization is carried out during training to reduce storage requirements, and spatio-temporal batch normalization (BN) operations are introduced to ensure high performance. During inference, a simple adaptive-threshold IF neuron model is proposed to achieve the effect equivalent to the computationally expensive spatio-temporal BN oper-ation without any performance loss. The proposed BN-free binarized SNNs based on hardware-saving IF neuron achieves competitive accuracies of 99.36%, 94.79%, 90.39%, and 67.10% on the N-MNIST, DvsGesture, N-TIDIGITS18, and DVS-CIFAR10 datasets, respectively, which are comparable to full-precision SNNs, but the weight sizes are significantly reduced by ti 97%. Furthermore, robustness exper-iments show that the binary SNN is more robust to weight noise than the full-precision SNN. This paper presents an efficient algorithm-hardware co-design paradigm for hardware-friendly and high -performance neuromorphic computing.(c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Hardware-friendly neural network
Neuromorphic computing
Surrogate gradient learning
Spiking neural network
Weight binarization
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Contributions of evacuation proctography and anorectal manometry to evaluation of adults with constipation and defecatory difficulty排空直肠造影和肛门直肠测压对成人便秘和排便困难的评估的贡献
PREAI
分享收藏A review of learning in biologically plausible spiking neural networks生物似是而非的尖峰神经网络学习综述.png)
NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏STBNN: Hardware-friendly spatio-temporal binary neural network with high pattern recognition accuracySTBNN: 硬件友好的高模式识别精度时空二进制神经网络NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏A Neuromorphic-Hardware Oriented Bio-Plausible Online-Learning Spiking Neural Network ModelIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Effect of Alkali-Metal Additive on Rudorffite AgBiI4-Based Pb-Free Solar Cells碱金属添加剂对Rudorffite AgBiI4基无铅太阳能电池的影响
PREAI
分享收藏
查看更多内容