1.1k citations · 2.2k across the 33 of their papers we have counts for
33 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…
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…
Continuous-Time Analog Filters for Audio Edge Intelligence: Review on Circuit Designs
Kwantae Kim, Shih-Chii Liu
Edge audio devices can reduce data bandwidth requirements by pre-processing input speech on the device before transmission to the cloud. As edge devices are required to ensure alwa…
Optimizing the Consumption of Spiking Neural Networks with Activity Regularization
Simon Narduzzi, Siavash A. Bigdeli, Shih-Chii Liu +1
Reducing energy consumption is a critical point for neural network models running on edge devices. In this regard, reducing the number of multiply-accumulate (MAC) operations of De…
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks
Yuhuang Hu, Shih-Chii Liu
Deep Neural Networks, particularly Convolutional Neural Networks (ConvNets), have achieved incredible success in many vision tasks, but they usually require millions of parameters…
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…
Exploiting Spatial Sparsity for Event Cameras with Visual Transformers
Zuowen Wang, Yuhuang Hu, Shih-Chii Liu
Event cameras report local changes of brightness through an asynchronous stream of output events. Events are spatially sparse at pixel locations with little brightness variation. W…
T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events
Shu Wang, Yuhuang Hu, Shih-Chii Liu
Spiking silicon cochlea sensors encode sound as an asynchronous stream of spikes from different frequency channels. The lack of labeled training datasets for spiking cochleas makes…
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…
Prospects for Analog Circuits in Deep Networks
Shih-Chii Liu, John Paul Strachan, Arindam Basu
Operations typically used in machine learning al-gorithms (e.g. adds and soft max) can be implemented bycompact analog circuits. Analog Application-Specific Integrated Circuit (ASI…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
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…
DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction
Yuhuang Hu, Jonathan Binas, Daniel Neil +2
Neuromorphic event cameras are useful for dynamic vision problems under difficult lighting conditions. To enable studies of using event cameras in automobile driving applications,…
Data-Driven Neuromorphic DRAM-based CNN and RNN Accelerators
Tobi Delbruck, Shih-Chii Liu
The energy consumed by running large deep neural networks (DNNs) on hardware accelerators is dominated by the need for lots of fast memory to store both states and weights. This la…
Learning to Exploit Multiple Vision Modalities by Using Grafted Networks
Yuhuang Hu, Tobi Delbruck, Shih-Chii Liu
Novel vision sensors such as thermal, hyperspectral, polarization, and event cameras provide information that is not available from conventional intensity cameras. An obstacle to u…
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…
Thermodynamic Computing
Tom Conte, Erik DeBenedictis, Natesh Ganesh +36
The hardware and software foundations laid in the first half of the 20th Century enabled the computing technologies that have transformed the world, but these foundations are now u…
FaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing
Yi Luo, Enea Ceolini, Cong Han +2
Beamforming has been extensively investigated for multi-channel audio processing tasks. Recently, learning-based beamforming methods, sometimes called \textit{neural beamformers},…
Closing the Accuracy Gap in an Event-Based Visual Recognition Task
Bodo Rückauer, Nicolas Känzig, Shih-Chii Liu +2
Mobile and embedded applications require neural networks-based pattern recognition systems to perform well under a tight computational budget. In contrast to commonly used synchron…
Reducing state updates via Gaussian-gated LSTMs
Matthew Thornton, Jithendar Anumula, Shih-Chii Liu
Recurrent neural networks can be difficult to train on long sequence data due to the well-known vanishing gradient problem. Some architectures incorporate methods to reduce RNN sta…
Overcoming the vanishing gradient problem in plain recurrent networks
Yuhuang Hu, Adrian Huber, Jithendar Anumula +1
Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU)…
DDD17: End-To-End DAVIS Driving Dataset
Jonathan Binas, Daniel Neil, Shih-Chii Liu +1
Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent s…
Sensor Transformation Attention Networks
Stefan Braun, Daniel Neil, Enea Ceolini +2
Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attention mechanisms into neural networks increases the…