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Found 11 papers, 2 papers with code
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems
We found, following experiments with wide residual networks applied to the ImageNet, CIFAR 10 and CIFAR 100 image classification datasets, that BN layers do not consistently offer a significant advantage.
Ranked #94 on
Image Classification
on CIFAR-100
(using extra training data)
Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.
An FPGA-based Massively Parallel Neuromorphic Cortex Simulator
This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex.
A Stochastic Approach to STDP
The decay generator will then generate an exponential decay, which will be used by the STDP adaptor to perform the weight adaption.
A Reconfigurable Mixed-signal Implementation of a Neuromorphic ADC
The architecture consists of an analogue chip and a control module.
A compact aVLSI conductance-based silicon neuron
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 neuromorphic systems.
A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition
The architecture is not limited to handwriting recognition, but is generally applicable as an extremely fast pattern recognition processor for various kinds of patterns such as speech and images.
A Trainable Neuromorphic Integrated Circuit that Exploits Device Mismatch
Additionally, we characterise each neuron and discuss the statistical variability of its tuning curve that arises due to random device mismatch, a desirable property for the learning capability of the TAB.
An Online Learning Algorithm for Neuromorphic Hardware Implementation
We propose a sign-based online learning (SOL) algorithm for a neuromorphic hardware framework called Trainable Analogue Block (TAB).
A neuromorphic hardware framework based on population coding
These neuronal populations are characterised by a diverse distribution of tuning curves, ensuring that the entire range of input stimuli is encoded.
The Ripple Pond: Enabling Spiking Networks to See
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 Networks (PCN), enables rapid, unsupervised, scale and rotation invariant object recognition using efficient spatio-temporal spike coding.
