98 citations · 233 across the 14 of their papers we have counts for
18 papers
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…
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…