145 research outputs found
Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
Surface electromyogram (sEMG) signals result from muscle movement and hence
they are an ideal candidate for benchmarking event-driven sensing and
computing. We propose a simple yet novel approach for optimizing the spike
encoding algorithm's hyper-parameters inspired by the readout layer concept in
reservoir computing. Using a simple machine learning algorithm after spike
encoding, we report performance higher than the state-of-the-art spiking neural
networks on two open-source datasets for hand gesture recognition. The spike
encoded data is processed through a spiking reservoir with a biologically
inspired topology and neuron model. When trained with the unsupervised activity
regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir
yields better performance than state-of-the-art convolutional neural networks.
The reservoir performance with regulated activity was found to be 89.72% for
the Roshambo EMG dataset and 70.6% for the EMG subset of sensor fusion dataset.
Therefore, the biologically-inspired computing paradigm, which is known for
being power efficient, also proves to have a great potential when compared with
conventional AI algorithms.Comment: Accepted to International Conference on Neuromorphic Systems (ICONS
2021
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic
processors, new opportunities are emerging for applying deep and Spiking Neural
Network (SNN) algorithms to healthcare and biomedical applications at the edge.
This can facilitate the advancement of the medical Internet of Things (IoT)
systems and Point of Care (PoC) devices. In this paper, we provide a tutorial
describing how various technologies ranging from emerging memristive devices,
to established Field Programmable Gate Arrays (FPGAs), and mature Complementary
Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL
accelerators to solve a wide variety of diagnostic, pattern recognition, and
signal processing problems in healthcare. Furthermore, we explore how spiking
neuromorphic processors can complement their DL counterparts for processing
biomedical signals. After providing the required background, we unify the
sparsely distributed research on neural network and neuromorphic hardware
implementations as applied to the healthcare domain. In addition, we benchmark
various hardware platforms by performing a biomedical electromyography (EMG)
signal processing task and drawing comparisons among them in terms of inference
delay and energy. Finally, we provide our analysis of the field and share a
perspective on the advantages, disadvantages, challenges, and opportunities
that different accelerators and neuromorphic processors introduce to healthcare
and biomedical domains. This paper can serve a large audience, ranging from
nanoelectronics researchers, to biomedical and healthcare practitioners in
grasping the fundamental interplay between hardware, algorithms, and clinical
adoption of these tools, as we shed light on the future of deep networks and
spiking neuromorphic processing systems as proponents for driving biomedical
circuits and systems forward.Comment: Submitted to IEEE Transactions on Biomedical Circuits and Systems (21
pages, 10 figures, 5 tables
Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures
Neuromorphic processors that implement Spiking Neural Networks (SNNs) using
mixed-signal analog/digital circuits represent a promising technology for
closed-loop real-time processing of biosignals. As in biology, to minimize
power consumption, the silicon neurons' circuits are configured to fire with a
limited dynamic range and with maximum firing rates restricted to a few tens or
hundreds of Herz.
However, biosignals can have a very large dynamic range, so encoding them
into spikes without saturating the neuron outputs represents an open challenge.
In this work, we present a biologically-inspired strategy for compressing
this high-dynamic range in SNN architectures, using three adaptation mechanisms
ubiquitous in the brain: spike-frequency adaptation at the single neuron level,
feed-forward inhibitory connections from neurons belonging to the input layer,
and Excitatory-Inhibitory (E-I) balance via recurrent inhibition among neurons
in the output layer.
We apply this strategy to input biosignals encoded using both an asynchronous
delta modulation method and an energy-based pulse-frequency modulation method.
We validate this approach in silico, simulating a simple network applied to a
gesture classification task from surface EMG recordings.Comment: 5 pages, 7 figures, to be published in IEEE BioCAS 2023 Proceeding
A spiking network classifies human sEMG signals and triggers finger reflexes on a robotic hand
The interaction between robots and humans is of great relevance for the field of neurorobotics as it can provide insights on how humans perform motor control and sensor processing and on how it can be applied to robotics. We propose a spiking neural network (SNN) to trigger finger motion reflexes on a robotic hand based on human surface Electromyography (sEMG) data. The first part of the network takes sEMG signals to measure muscle activity, then classify the data to detect which finger is being flexed in the human hand. The second part triggers single finger reflexes on the robot using the classification output. The finger reflexes are modeled with motion primitives activated with an oscillator and mapped to the robot kinematic. We evaluated the SNN by having users wear a non-invasive sEMG sensor, record a training dataset, and then flex different fingers, one at a time. The muscle activity was recorded using a Myo sensor with eight different channels. The sEMG signals were successfully encoded into spikes as input for the SNN. The classification could detect the active finger and trigger the motion generation of finger reflexes. The SNN was able to control a real Schunk SVH 5-finger robotic hand online. Being able to map myo-electric activity to functions of motor control for a task, can provide an interesting interface for robotic applications, and a platform to study brain functioning. SNN provide a challenging but interesting framework to interact with human data. In future work the approach will be extended to control also a robot arm at the same time
Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips
Deep Neural Networks (DNN) achieve human level performance in many image
analytics tasks but DNNs are mostly deployed to GPU platforms that consume a
considerable amount of power. New hardware platforms using lower precision
arithmetic achieve drastic reductions in power consumption. More recently,
brain-inspired spiking neuromorphic chips have achieved even lower power
consumption, on the order of milliwatts, while still offering real-time
processing.
However, for deploying DNNs to energy efficient neuromorphic chips the
incompatibility between continuous neurons and synaptic weights of traditional
DNNs, discrete spiking neurons and synapses of neuromorphic chips need to be
overcome. Previous work has achieved this by training a network to learn
continuous probabilities, before it is deployed to a neuromorphic architecture,
such as IBM TrueNorth Neurosynaptic System, by random sampling these
probabilities.
The main contribution of this paper is a new learning algorithm that learns a
TrueNorth configuration ready for deployment. We achieve this by training
directly a binary hardware crossbar that accommodates the TrueNorth axon
configuration constrains and we propose a different neuron model.
Results of our approach trained on electroencephalogram (EEG) data show a
significant improvement with previous work (76% vs 86% accuracy) while
maintaining state of the art performance on the MNIST handwritten data set.Comment: IJCAI-2017. arXiv admin note: text overlap with arXiv:1605.0774
Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing
Reservoir computing (RC) is gaining traction in several signal processing domains, owing to its nonlinear stateful computation, spatiotemporal encoding, and reduced training complexity over recurrent neural networks (RNNs). Previous studies have shown the effectiveness of software-based RCs for a wide spectrum of applications. A parallel body of work indicates that realizing RNN architectures using custom integrated circuits and reconfigurable hardware platforms yields significant improvements in power and latency. In this research, we propose a neuromemristive RC architecture, with doubly twisted toroidal structure, that is validated for biosignal processing applications. We exploit the device mismatch to implement the random weight distributions within the reservoir and propose mixed-signal subthreshold circuits for energy efficiency. A comprehensive analysis is performed to compare the efficiency of the neuromemristive RC architecture in both digital(reconfigurable) and subthreshold mixed-signal realizations. Both EEG and EMG biosignal benchmarks are used for validating the RC designs. The proposed RC architecture demonstrated an accuracy of 90% and 84% for epileptic seizure detection and EMG prosthetic finger control respectively
Optimisation de réseaux de neurones à décharges avec contraintes matérielles pour processeur neuromorphique
Les modèles informatiques basés sur l'apprentissage machine ont démarré la seconde révolution de l'intelligence artificielle. Capables d'atteindre des performances que l'on crut inimaginables au préalable, ces modèles semblent devenir partie courante dans plusieurs domaines. La face cachée de ceux-ci est que l'énergie consommée pour l'apprentissage, et l'utilisation de ces techniques, est colossale. La dernière décennie a été marquée par l'arrivée de plusieurs processeurs neuromorphiques pouvant simuler des réseaux de neurones avec une faible consommation d'énergie. Ces processeurs offrent une alternative aux conventionnelles cartes graphiques qui demeurent à ce jour essentielles au domaine. Ces processeurs sont capables de réduire la consommation d'énergie en utilisant un modèle de neurone événementiel, plus communément appelé neurone à décharge. Ce type de neurone est fondamentalement différent du modèle classique, et possède un aspect temporel important. Les méthodes, algorithmes et outils développés pour le modèle de neurone classique ne sont pas adaptés aux neurones à décharges. Cette thèse de doctorat décrit plusieurs approches fondamentales, dédiées à la création de processeurs neuromorphiques analogiques, qui permettent de pallier l'écart existant entre les systèmes à base de neurones conventionnels et à décharges. Dans un premier temps, nous présentons une nouvelle règle de plasticité synaptique permettant l'apprentissage non supervisé des réseaux de neurones récurrents utilisant ce nouveau type de neurone. Puis, nous proposons deux nouvelles méthodes pour la conception des topologies de ce même type de réseau. Finalement, nous améliorons les techniques d'apprentissage supervisé en augmentant la capacité de mémoire de réseaux récurrents. Les éléments de cette thèse marient l'inspiration biologique du cerveau, l'ingénierie neuromorphique et l'informatique fondamentale pour permettre d'optimiser les réseaux de neurones pouvant fonctionner sur des processeurs neuromorphiques analogiques
Neuromorphic Online Learning for Spatiotemporal Patterns with a Forward-only Timeline
Spiking neural networks (SNNs) are bio-plausible computing models with high
energy efficiency. The temporal dynamics of neurons and synapses enable them to
detect temporal patterns and generate sequences. While Backpropagation Through
Time (BPTT) is traditionally used to train SNNs, it is not suitable for online
learning of embedded applications due to its high computation and memory cost
as well as extended latency. Previous works have proposed online learning
algorithms, but they often utilize highly simplified spiking neuron models
without synaptic dynamics and reset feedback, resulting in subpar performance.
In this work, we present Spatiotemporal Online Learning for Synaptic Adaptation
(SOLSA), specifically designed for online learning of SNNs composed of Leaky
Integrate and Fire (LIF) neurons with exponentially decayed synapses and soft
reset. The algorithm not only learns the synaptic weight but also adapts the
temporal filters associated to the synapses. Compared to the BPTT algorithm,
SOLSA has much lower memory requirement and achieves a more balanced temporal
workload distribution. Moreover, SOLSA incorporates enhancement techniques such
as scheduled weight update, early stop training and adaptive synapse filter,
which speed up the convergence and enhance the learning performance. When
compared to other non-BPTT based SNN learning, SOLSA demonstrates an average
learning accuracy improvement of 14.2%. Furthermore, compared to BPTT, SOLSA
achieves a 5% higher average learning accuracy with a 72% reduction in memory
cost.Comment: 9 pages,8 figure
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