15 research outputs found
Supervised learning in Spiking Neural Networks with Limited Precision: SNN/LP
A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural
Networks. This novel algorithm uses limited precision for both synaptic weights
and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for
the supervised training. The results are comparable or better than previously
published work. The results are applicable to the realization of large scale
hardware neural networks. One of the trained networks is implemented in
programmable hardware.Comment: 7 pages, originally submitted to IJCNN 201
Hybrid Neural Network, An Efficient Low-Power Digital Hardware Implementation of Event-based Artificial Neural Network
Interest in event-based vision sensors has proliferated
in recent years, with innovative technology becoming more
accessible to new researchers and highlighting such sensors’
potential to enable low-latency sensing at low computational
cost. These sensors can outperform frame-based vision sensors
regarding data compression, dynamic range, temporal resolution
and power efficiency. However, available mature framebased
processing methods by using Artificial Neural Networks
(ANNs) surpass Spiking Neural Networks (SNNs) in terms of
accuracy of recognition. In this paper, we introduce a Hybrid
Neural Network which is an intermediate solution to exploit
advantages of both event-based and frame-based processing.We
have implemented this network in FPGA and benchmarked its
performance by using different event-based versions of MNIST
dataset. HDL codes for this project are available for academic
purpose upon request
Benchmarking spike-based visual recognition: a dataset and evaluation
Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organisation have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarks and that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware implementations. With this dataset we hope to (1) promote meaningful comparison between algorithms in the field of neural computation, (2) allow comparison with conventional image recognition methods, (3) provide an assessment of the state of the art in spike-based visual recognition, and (4) help researchers identify future directions and advance the field
An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data
This paper introduces a novel methodology for training an event-driven classifier within
a Spiking Neural Network (SNN) System capable of yielding good classification results
when using both synthetic input data and real data captured from Dynamic Vision Sensor
(DVS) chips. The proposed supervised method uses the spiking activity provided by an
arbitrary topology of prior SNN layers to build histograms and train the classifier in the
frame domain using the stochastic gradient descent algorithm. In addition, this approach
can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature
for real-world SNN applications, where neural activation must fade away after some
time in the absence of inputs. Consequently, this way of building histograms captures
the dynamics of spikes immediately before the classifier. We tested our method on
the MNIST data set using different synthetic encodings and real DVS sensory data
sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology
and feature maps. We demonstrate the effectiveness of our approach by achieving
the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS
(100%) real DVS data sets to date with a spiking convolutional network. Moreover, by
using the proposed method we were able to retrain the output layer of a previously
reported spiking neural network and increase its performance by 2%, suggesting that
the proposed classifier can be used as the output layer in works where features are
extracted using unsupervised spike-based learningmethods. In addition, we also analyze
SNN performance figures such as total event activity and network latencies, which
are relevant for eventual hardware implementations. In summary, the paper aggregates
unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and
applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS
chips.Peer reviewe
An event-based classifier for Dynamic Vision Sensor and synthetic data
This paper introduces a novel methodology for training an event-driven classifier within
a Spiking Neural Network (SNN) System capable of yielding good classification results
when using both synthetic input data and real data captured from Dynamic Vision Sensor
(DVS) chips. The proposed supervised method uses the spiking activity provided by an
arbitrary topology of prior SNN layers to build histograms and train the classifier in the
frame domain using the stochastic gradient descent algorithm. In addition, this approach
can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature
for real-world SNN applications, where neural activation must fade away after some
time in the absence of inputs. Consequently, this way of building histograms captures
the dynamics of spikes immediately before the classifier. We tested our method on
the MNIST data set using different synthetic encodings and real DVS sensory data
sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology
and feature maps. We demonstrate the effectiveness of our approach by achieving
the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS
(100%) real DVS data sets to date with a spiking convolutional network. Moreover, by
using the proposed method we were able to retrain the output layer of a previously
reported spiking neural network and increase its performance by 2%, suggesting that
the proposed classifier can be used as the output layer in works where features are
extracted using unsupervised spike-based learningmethods. In addition, we also analyze
SNN performance figures such as total event activity and network latencies, which
are relevant for eventual hardware implementations. In summary, the paper aggregates
unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and
applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS
chips.Samsung Advanced Institute of Technology EU H2020 644096Samsung Advanced Institute of Technology EU H2020 687299Ministerio de Economía y Competitividad TEC2012-37868-C04-01Ministerio de Economía y Competitividad TEC2015-63884-C2-1-PJunta de Andalucía TIC-609