158 citations · 895 across the 103 of their papers we have counts for
7 papers · 1 filter
Training Spiking Neural Networks with Local Tandem Learning
Qu Yang, Jibin Wu, Malu Zhang +3
Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient over their predecessors. However, there is a lack of an efficient and generalized tr…
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
Jibin Wu, Chenglin Xu, Daquan Zhou +2
Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency, due to their event-…
You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy
Srivatsa P, Kyle Timothy Ng Chu, Burin Amornpaisannon +6
In the past decade, advances in Artificial Neural Networks (ANNs) have allowed them to perform extremely well for a wide range of tasks. In fact, they have reached human parity whe…
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
Malu Zhang, Jiadong Wang, Burin Amornpaisannon +8
Spiking Neural Networks (SNNs) use spatio-temporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-po…
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
Jibin Wu, Emre Yilmaz, Malu Zhang +2
Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi…
Neural Population Coding for Effective Temporal Classification
Zihan Pan, Jibin Wu, Yansong Chua +2
Neural encoding plays an important role in faithfully describing the temporally rich patterns, whose instances include human speech and environmental sounds. For tasks that involve…
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Jibin Wu, Yansong Chua, Malu Zhang +3
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures. However, due to the non-differentia…