551 research outputs found
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
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
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
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
A Survey of Spiking Neural Network Accelerator on FPGA
Due to the ability to implement customized topology, FPGA is increasingly
used to deploy SNNs in both embedded and high-performance applications. In this
paper, we survey state-of-the-art SNN implementations and their applications on
FPGA. We collect the recent widely-used spiking neuron models, network
structures, and signal encoding formats, followed by the enumeration of related
hardware design schemes for FPGA-based SNN implementations. Compared with the
previous surveys, this manuscript enumerates the application instances that
applied the above-mentioned technical schemes in recent research. Based on
that, we discuss the actual acceleration potential of implementing SNN on FPGA.
According to our above discussion, the upcoming trends are discussed in this
paper and give a guideline for further advancement in related subjects
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
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