Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons' energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs). Here, we demonstrate how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields state-of-the-art for SNNs on challenging benchmarks in the time-domain, like speech and gesture recognition. This also exceeds the performance of standard classical recurrent neural networks (RNNs) and approaches that of the best modern ANNs. As these SNNs exhibit sparse spiking, we show that they theoretically are one to three orders of magnitude more computationally efficient compared to RNNs with comparable performance. Together, this positions SNNs as an attractive solution for AI hardware implementations.
Code (0)
등록된 구현이 없습니다.
Tasks
Audio Classificationdomain classificationGeneral ClassificationGesture RecognitionSimilar Papers 제목 키워드 기반
Accurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons
Thanks to their parallel and sparse activity features, recurrent neural networks (RNNs) are well-suited for hardware implementation in low-power neuromorphic hardware. However, mapping rate-based RNNs to hardware-compati…
Edge-computingEfficient Converted Spiking Neural Network for 3D and 2D Classification
Spiking Neural Networks (SNNs) have attracted enormous research interest due to their low-power and biologically plausible nature. Existing ANN-SNN conversion methods can achieve lossless conversion by converting a w…
image-classificationImage ClassificationPoint Cloud ClassificationSpiking Neural Networks for Mental Workload Classification with a Multimodal Approach
Accurately assessing mental workload is crucial in cognitive neuroscience, human-computer interaction, and real-time monitoring, as cognitive load fluctuations affect performance and decision-making. While Electroencepha…
When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron
Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. However, most existing SNN-based methods for image tasks do not fully exploit this feature. More…
On robot compliance. A cerebellar control approach
The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driv…