1,680 research outputs found
Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
Spike-timing-dependent plasticity (STDP) incurs both causal and acausal
synaptic weight updates, for negative and positive time differences between
pre-synaptic and post-synaptic spike events. For realizing such updates in
neuromorphic hardware, current implementations either require forward and
reverse lookup access to the synaptic connectivity table, or rely on
memory-intensive architectures such as crossbar arrays. We present a novel
method for realizing both causal and acausal weight updates using only forward
lookup access of the synaptic connectivity table, permitting memory-efficient
implementation. A simplified implementation in FPGA, using a single timer
variable for each neuron, closely approximates exact STDP cumulative weight
updates for neuron refractory periods greater than 10 ms, and reduces to exact
STDP for refractory periods greater than the STDP time window. Compared to
conventional crossbar implementation, the forward table-based implementation
leads to substantial memory savings for sparsely connected networks supporting
scalable neuromorphic systems with fully reconfigurable synaptic connectivity
and plasticity.Comment: Submitted to BioCAS 201
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight
quantization as a means to increase the resource- and power-efficiency of
hardware devices. As full on-chip weight storage is necessary to avoid the high
energy cost of off-chip memory accesses, memory reduction requirements for
weight storage pushed toward the use of binary weights, which were demonstrated
to have a limited accuracy reduction on many applications when
quantization-aware training techniques are used. In parallel, spiking neural
network (SNN) architectures are explored to further reduce power when
processing sparse event-based data streams, while on-chip spike-based online
learning appears as a key feature for applications constrained in power and
resources during the training phase. However, designing power- and
area-efficient spiking neural networks still requires the development of
specific techniques in order to leverage on-chip online learning on binary
weights without compromising the synapse density. In this work, we demonstrate
MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a
stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning
rule and a hierarchical routing fabric for large-scale chip interconnection.
The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF)
neurons and more than two million plastic synapses for an active silicon area
of 2.86mm in 65nm CMOS, achieving a high density of 738k synapses/mm.
MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy
tradeoff on the MNIST classification task compared to previously-proposed SNNs,
while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE
Transactions on Biomedical Circuits and Systems journal (2019), the
fully-edited paper is available at
https://ieeexplore.ieee.org/document/876400
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP
Humans perform remarkably well in many cognitive tasks including pattern
recognition. However, the neuronal mechanisms underlying this process are not
well understood. Nevertheless, artificial neural networks, inspired in brain
circuits, have been designed and used to tackle spatio-temporal pattern
recognition tasks. In this paper we present a multineuronal spike pattern
detection structure able to autonomously implement online learning and
recognition of parallel spike sequences (i.e., sequences of pulses belonging to
different neurons/neural ensembles). The operating principle of this structure
is based on two spiking/synaptic neurocomputational characteristics: spike
latency, that enables neurons to fire spikes with a certain delay and
heterosynaptic plasticity, that allows the own regulation of synaptic weights.
From the perspective of the information representation, the structure allows
mapping a spatio-temporal stimulus into a multidimensional, temporal, feature
space. In this space, the parameter coordinate and the time at which a neuron
fires represent one specific feature. In this sense, each feature can be
considered to span a single temporal axis. We applied our proposed scheme to
experimental data obtained from a motor inhibitory cognitive task. The test
exhibits good classification performance, indicating the adequateness of our
approach. In addition to its effectiveness, its simplicity and low
computational cost suggest a large scale implementation for real time
recognition applications in several areas, such as brain computer interface,
personal biometrics authentication or early detection of diseases.Comment: Submitted to Frontiers in Neuroscienc
Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach
Speech recognition has become an important task
to improve the human-machine interface. Taking into account
the limitations of current automatic speech recognition systems,
like non-real time cloud-based solutions or power demand,
recent interest for neural networks and bio-inspired systems has
motivated the implementation of new techniques.
Among them, a combination of spiking neural networks and
neuromorphic auditory sensors offer an alternative to carry
out the human-like speech processing task. In this approach,
a spiking convolutional neural network model was implemented,
in which the weights of connections were calculated by training
a convolutional neural network with specific activation functions,
using firing rate-based static images with the spiking information
obtained from a neuromorphic cochlea.
The system was trained and tested with a large dataset
that contains ”left” and ”right” speech commands, achieving
89.90% accuracy. A novel spiking neural network model has been
proposed to adapt the network that has been trained with static
images to a non-static processing approach, making it possible
to classify audio signals and time series in real time.Ministerio de Economía y Competitividad TEC2016-77785-
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