1,046 research outputs found
Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
Recent advances in Voice Activity Detection (VAD) are driven by artificial
and Recurrent Neural Networks (RNNs), however, using a VAD system in
battery-operated devices requires further power efficiency. This can be
achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs)
to perform inference at very low energy consumption. Spiking networks are
characterized by their ability to process information efficiently, in a sparse
cascade of binary events in time called spikes. However, a big performance gap
separates artificial from spiking networks, mostly due to a lack of powerful
SNN training algorithms. To overcome this problem we exploit an SNN model that
can be recast into an RNN-like model and trained with known deep learning
techniques. We describe an SNN training procedure that achieves low spiking
activity and pruning algorithms to remove 85% of the network connections with
no performance loss. The model achieves state-of-the-art performance with a
fraction of power consumption comparing to other methods.Comment: 5 pages, 2 figures, 2 table
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware
In recent years the field of neuromorphic low-power systems that consume
orders of magnitude less power gained significant momentum. However, their
wider use is still hindered by the lack of algorithms that can harness the
strengths of such architectures. While neuromorphic adaptations of
representation learning algorithms are now emerging, efficient processing of
temporal sequences or variable length-inputs remain difficult. Recurrent neural
networks (RNN) are widely used in machine learning to solve a variety of
sequence learning tasks. In this work we present a train-and-constrain
methodology that enables the mapping of machine learned (Elman) RNNs on a
substrate of spiking neurons, while being compatible with the capabilities of
current and near-future neuromorphic systems. This "train-and-constrain" method
consists of first training RNNs using backpropagation through time, then
discretizing the weights and finally converting them to spiking RNNs by
matching the responses of artificial neurons with those of the spiking neurons.
We demonstrate our approach by mapping a natural language processing task
(question classification), where we demonstrate the entire mapping process of
the recurrent layer of the network on IBM's Neurosynaptic System "TrueNorth", a
spike-based digital neuromorphic hardware architecture. TrueNorth imposes
specific constraints on connectivity, neural and synaptic parameters. To
satisfy these constraints, it was necessary to discretize the synaptic weights
and neural activities to 16 levels, and to limit fan-in to 64 inputs. We find
that short synaptic delays are sufficient to implement the dynamical (temporal)
aspect of the RNN in the question classification task. The hardware-constrained
model achieved 74% accuracy in question classification while using less than
0.025% of the cores on one TrueNorth chip, resulting in an estimated power
consumption of ~17 uW
- …