62 citations · 286 across the 10 of their papers we have counts for
10 papers
3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network
Qinyu Chen, Zuowen Wang, Shih-Chii Liu +1
This paper presents a sparse Change-Based Convolutional Long Short-Term Memory (CB-ConvLSTM) model for event-based eye tracking, key for next-generation wearable healthcare technol…
To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration
Fabrizio Ottati, Chang Gao, Qinyu Chen +4
As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense…
Real-time Path Planning of Driver-less Mining Trains with Time-dependent Physical Constraints
Xiaojiang Ren, Hui Guo, Sheng Kai +1
While the increased automation levels of production and operation equipment have led to improved productivity of mining activity in open pit mines, the capacity of mine transport s…
A 23 W Keyword Spotting IC with Ring-Oscillator-Based Time-Domain Feature Extraction
Kwantae Kim, Chang Gao, Rui Graça +4
This article presents the first keyword spotting (KWS) IC which uses a ring-oscillator-based time-domain processing technique for its analog feature extractor (FEx). Its extensive…
Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance
Qinyu Chen, Chang Gao, Xinyuan Fang +1
Spiking Neural Networks (SNNs) are developed as a promising alternative to Artificial Neural networks (ANNs) due to their more realistic brain-inspired computing models. SNNs have…
Spiking Cochlea with System-level Local Automatic Gain Control
Ilya Kiselev, Chang Gao, Shih-Chii Liu
Including local automatic gain control (AGC) circuitry into a silicon cochlea design has been challenging because of transistor mismatch and model complexity. To address this, we p…
Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity
Chang Gao, Tobi Delbruck, Shih-Chii Liu
Long Short-Term Memory (LSTM) recurrent networks are frequently used for tasks involving time-sequential data such as speech recognition. Unlike previous LSTM accelerators that eit…
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…
Recurrent Neural Network Control of a Hybrid Dynamic Transfemoral Prosthesis with EdgeDRNN Accelerator
Chang Gao, Rachel Gehlhar, Aaron D. Ames +2
Lower leg prostheses could improve the life quality of amputees by increasing comfort and reducing energy to locomote, but currently control methods are limited in modulating behav…
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…