62 citations · 80 across the 4 of their papers we have counts for
4 papers
Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training
Xi Chen, Chang Gao, Zuowen Wang +4
Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large…
EEGSN: Towards Efficient Low-latency Decoding of EEG with Graph Spiking Neural Networks
Xi Chen, Siwei Mai, Konstantinos Michmizos
A vast majority of spiking neural networks (SNNs) are trained based on inductive biases that are not necessarily a good fit for several critical tasks that require low-latency and…
EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference
Chang Gao, Antonio Rios-Navarro, Xi Chen +2
Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful inference capabilities for real-time applications such as IoT, robotics, and human-machin…
EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference
Chang Gao, Antonio Rios-Navarro, Xi Chen +2
This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing. EdgeDRNN adopts the spiking…