108 citations · 259 across the 22 of their papers we have counts for
18 papers · 1 filter
RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge
Adithya Krishna, Srikanth Rohit Nudurupati, Chandana D G +4
Deep Neural Network (DNN) based inference at the edge is challenging as these compute and data-intensive algorithms need to be implemented at low cost and low power while meeting t…
Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA
Ali Mehrabi, Yeshwanth Bethi, André van Schaik +2
This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first networ…
An optimised deep spiking neural network architecture without gradients
Yeshwanth Bethi, Ying Xu, Gregory Cohen +2
We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules to perform transformations between arbitrary spa…
A Stochastic Approach to STDP
Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton +2
We present a digital implementation of the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed digital implementation consists of an exponential decay generator ar…
Investigation of event-based memory surfaces for high-speed tracking, unsupervised feature extraction and object recognition
Saeed Afshar, Gregory Cohen, Tara Julia Hamilton +2
In this paper we compare event-based decaying and time based-decaying memory surfaces for high-speed eventbased tracking, feature extraction, and object classification using an eve…
A Reconfigurable Mixed-signal Implementation of a Neuromorphic ADC
Ying Xu, Chetan Singh Thakur, Tara Julia Hamilton +3
We present a neuromorphic Analogue-to-Digital Converter (ADC), which uses integrate-and-fire (I&F) neurons as the encoders of the analogue signal, with modulated inhibitions to dec…
A compact aVLSI conductance-based silicon neuron
Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton +2
We present an analogue Very Large Scale Integration (aVLSI) implementation that uses first-order lowpass filters to implement a conductance-based silicon neuron for high-speed neur…
A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition
Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton +2
We present a hardware architecture that uses the Neural Engineering Framework (NEF) to implement large-scale neural networks on Field Programmable Gate Arrays (FPGAs) for performin…
An Online Learning Algorithm for Neuromorphic Hardware Implementation
Chetan Singh Thakur, Runchun Wang, Saeed Afshar +4
We propose a sign-based online learning (SOL) algorithm for a neuromorphic hardware framework called Trainable Analogue Block (TAB). The TAB framework utilises the principles of ne…
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm
Mark D. McDonnell, Migel D. Tissera, Tony Vladusich +2
Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default op…
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…
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…
Explicit Computation of Input Weights in Extreme Learning Machines
Jonathan Tapson, Philip de Chazal, André van Schaik
We present a closed form expression for initializing the input weights in a multi-layer perceptron, which can be used as the first step in synthesis of an Extreme Learning Ma-chine…
ELM Solutions for Event-Based Systems
Jonathan Tapson, André van Schaik
Whilst most engineered systems use signals that are continuous in time, there is a domain of systems in which signals consist of events. Events, like Dirac delta functions, have no…
Online and Adaptive Pseudoinverse Solutions for ELM Weights
André van Schaik, Jonathan Tapson
The ELM method has become widely used for classification and regressions problems as a result of its accuracy, simplicity and ease of use. The solution of the hidden layer weights…
The Ripple Pond: Enabling Spiking Networks to See
Saeed Afshar, Gregory Cohen, Runchun Wang +4
In this paper we present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network that, operating together with recently proposed PolyChronous…
Learning the Pseudoinverse Solution to Network Weights
Jonathan Tapson, Andre van Schaik
The last decade has seen the parallel emergence in computational neuroscience and machine learning of neural network structures which spread the input signal randomly to a higher d…