9 citations · 10 across the 9 of their papers we have counts for
9 papers
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems
Mark D. McDonnell, Hesham Mostafa, Runchun Wang +1
Batch-normalization (BN) layers are thought to be an integrally important layer type in today's state-of-the-art deep convolutional neural networks for computer vision tasks such a…
A Trainable Neuromorphic Integrated Circuit that Exploits Device Mismatch
Chetan Singh Thakur, Runchun Wang, Tara Julia Hamilton +2
Random device mismatch that arises as a result of scaling of the CMOS (complementary metal-oxide semi-conductor) technology into the deep submicron regime degrades the accuracy of…
Turn Down that Noise: Synaptic Encoding of Afferent SNR in a Single Spiking Neuron
Saeed Afshar, Libin George, Jonathan Tapson +3
We have added a simplified neuromorphic model of Spike Time Dependent Plasticity (STDP) to the Synapto-dendritic Kernel Adapting Neuron (SKAN). The resulting neuron model is the fi…
Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels
Saeed Afshar, Libin George, Jonathan Tapson +2
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotempo…
Bayesian Inference with Spiking Neurons
Michael G. Paulin, Andre van Schaik
Humans and other animals behave as if we perform fast Bayesian inference underlying decisions and movement control given uncertain sense data. Here we show that a biophysically rea…
Learning ELM network weights using linear discriminant analysis
Philip de Chazal, Jonathan Tapson, André van Schaik
We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks. The method…