55 research outputs found
Spiking Neural Networks -- Part III: Neuromorphic Communications
Synergies between wireless communications and artificial intelligence are
increasingly motivating research at the intersection of the two fields. On the
one hand, the presence of more and more wirelessly connected devices, each with
its own data, is driving efforts to export advances in machine learning (ML)
from high performance computing facilities, where information is stored and
processed in a single location, to distributed, privacy-minded, processing at
the end user. On the other hand, ML can address algorithm and model deficits in
the optimization of communication protocols. However, implementing ML models
for learning and inference on battery-powered devices that are connected via
bandwidth-constrained channels remains challenging. This paper explores two
ways in which Spiking Neural Networks (SNNs) can help address these open
problems. First, we discuss federated learning for the distributed training of
SNNs, and then describe the integration of neuromorphic sensing, SNNs, and
impulse radio technologies for low-power remote inference.Comment: Submitte
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks
Spiking Neural Networks (SNNs) have gained increasing attention as
energy-efficient neural networks owing to their binary and asynchronous
computation. However, their non-linear activation, that is
Leaky-Integrate-and-Fire (LIF) neuron, requires additional memory to store a
membrane voltage to capture the temporal dynamics of spikes. Although the
required memory cost for LIF neurons significantly increases as the input
dimension goes larger, a technique to reduce memory for LIF neurons has not
been explored so far. To address this, we propose a simple and effective
solution, EfficientLIF-Net, which shares the LIF neurons across different
layers and channels. Our EfficientLIF-Net achieves comparable accuracy with the
standard SNNs while bringing up to ~4.3X forward memory efficiency and ~21.9X
backward memory efficiency for LIF neurons. We conduct experiments on various
datasets including CIFAR10, CIFAR100, TinyImageNet, ImageNet-100, and
N-Caltech101. Furthermore, we show that our approach also offers advantages on
Human Activity Recognition (HAR) datasets, which heavily rely on temporal
information
PrivateSNN: Privacy-Preserving Spiking Neural Networks
How can we bring both privacy and energy-efficiency to a neural system? In
this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural
Networks (SNNs) from a pre-trained ANN model without leaking sensitive
information contained in a dataset. Here, we tackle two types of leakage
problems: 1) Data leakage is caused when the networks access real training data
during an ANN-SNN conversion process. 2) Class leakage is caused when
class-related features can be reconstructed from network parameters. In order
to address the data leakage issue, we generate synthetic images from the
pre-trained ANNs and convert ANNs to SNNs using the generated images. However,
converted SNNs remain vulnerable to class leakage since the weight parameters
have the same (or scaled) value with respect to ANN parameters. Therefore, we
encrypt SNN weights by training SNNs with a temporal spike-based learning rule.
Updating weight parameters with temporal data makes SNNs difficult to be
interpreted in the spatial domain. We observe that the encrypted PrivateSNN
eliminates data and class leakage issues with a slight performance drop (less
than ~2) and significant energy-efficiency gain (about 55x) compared to the
standard ANN. We conduct extensive experiments on various datasets including
CIFAR10, CIFAR100, and TinyImageNet, highlighting the importance of
privacy-preserving SNN training.Comment: Accepted to AAAI202
Towards Efficient and Trustworthy AI Through Hardware-Algorithm-Communication Co-Design
Artificial intelligence (AI) algorithms based on neural networks have been
designed for decades with the goal of maximising some measure of accuracy. This
has led to two undesired effects. First, model complexity has risen
exponentially when measured in terms of computation and memory requirements.
Second, state-of-the-art AI models are largely incapable of providing
trustworthy measures of their uncertainty, possibly `hallucinating' their
answers and discouraging their adoption for decision-making in sensitive
applications.
With the goal of realising efficient and trustworthy AI, in this paper we
highlight research directions at the intersection of hardware and software
design that integrate physical insights into computational substrates,
neuroscientific principles concerning efficient information processing,
information-theoretic results on optimal uncertainty quantification, and
communication-theoretic guidelines for distributed processing. Overall, the
paper advocates for novel design methodologies that target not only accuracy
but also uncertainty quantification, while leveraging emerging computing
hardware architectures that move beyond the traditional von Neumann digital
computing paradigm to embrace in-memory, neuromorphic, and quantum computing
technologies. An important overarching principle of the proposed approach is to
view the stochasticity inherent in the computational substrate and in the
communication channels between processors as a resource to be leveraged for the
purpose of representing and processing classical and quantum uncertainty
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
The latest satellite communication (SatCom) missions are characterized by a
fully reconfigurable on-board software-defined payload, capable of adapting
radio resources to the temporal and spatial variations of the system traffic.
As pure optimization-based solutions have shown to be computationally tedious
and to lack flexibility, machine learning (ML)-based methods have emerged as
promising alternatives. We investigate the application of energy-efficient
brain-inspired ML models for on-board radio resource management. Apart from
software simulation, we report extensive experimental results leveraging the
recently released Intel Loihi 2 chip. To benchmark the performance of the
proposed model, we implement conventional convolutional neural networks (CNN)
on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy,
precision, recall, and energy efficiency for different traffic demands. Most
notably, for relevant workloads, spiking neural networks (SNNs) implemented on
Loihi 2 yield higher accuracy, while reducing power consumption by more than
100 as compared to the CNN-based reference platform. Our findings point
to the significant potential of neuromorphic computing and SNNs in supporting
on-board SatCom operations, paving the way for enhanced efficiency and
sustainability in future SatCom systems.Comment: currently under review at IEEE Transactions on Machine Learning in
Communications and Networkin
Dimensions of Timescales in Neuromorphic Computing Systems
This article is a public deliverable of the EU project "Memory technologies
with multi-scale time constants for neuromorphic architectures" (MeMScales,
https://memscales.eu, Call ICT-06-2019 Unconventional Nanoelectronics, project
number 871371). This arXiv version is a verbatim copy of the deliverable
report, with administrative information stripped. It collects a wide and varied
assortment of phenomena, models, research themes and algorithmic techniques
that are connected with timescale phenomena in the fields of computational
neuroscience, mathematics, machine learning and computer science, with a bias
toward aspects that are relevant for neuromorphic engineering. It turns out
that this theme is very rich indeed and spreads out in many directions which
defy a unified treatment. We collected several dozens of sub-themes, each of
which has been investigated in specialized settings (in the neurosciences,
mathematics, computer science and machine learning) and has been documented in
its own body of literature. The more we dived into this diversity, the more it
became clear that our first effort to compose a survey must remain sketchy and
partial. We conclude with a list of insights distilled from this survey which
give general guidelines for the design of future neuromorphic systems
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
The ongoing amalgamation of UAV and ML techniques is creating a significant
synergy and empowering UAVs with unprecedented intelligence and autonomy. This
survey aims to provide a timely and comprehensive overview of ML techniques
used in UAV operations and communications and identify the potential growth
areas and research gaps. We emphasise the four key components of UAV operations
and communications to which ML can significantly contribute, namely, perception
and feature extraction, feature interpretation and regeneration, trajectory and
mission planning, and aerodynamic control and operation. We classify the latest
popular ML tools based on their applications to the four components and conduct
gap analyses. This survey also takes a step forward by pointing out significant
challenges in the upcoming realm of ML-aided automated UAV operations and
communications. It is revealed that different ML techniques dominate the
applications to the four key modules of UAV operations and communications.
While there is an increasing trend of cross-module designs, little effort has
been devoted to an end-to-end ML framework, from perception and feature
extraction to aerodynamic control and operation. It is also unveiled that the
reliability and trust of ML in UAV operations and applications require
significant attention before full automation of UAVs and potential cooperation
between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure
Local learning algorithms for stochastic spiking neural networks
This dissertation focuses on the development of machine learning algorithms for spiking neural networks, with an emphasis on local three-factor learning rules that are in keeping with the constraints imposed by current neuromorphic hardware. Spiking neural networks (SNNs) are an alternative to artificial neural networks (ANNs) that follow a similar graphical structure but use a processing paradigm more closely modeled after the biological brain in an effort to harness its low power processing capability. SNNs use an event based processing scheme which leads to significant power savings when implemented in dedicated neuromorphic hardware such as Intel’s Loihi chip.
This work is distinguished by the consideration of stochastic SNNs based on spiking neurons that employ a stochastic spiking process, implementing generalized linear models (GLM) rather than deterministic thresholded spiking. In this framework, the spiking signals are random variables which may be sampled from a distribution defined by the neurons. The spiking signals may be observed or latent variables, with neurons whose outputs are observed termed visible neurons and otherwise termed hidden neurons. This choice provides a strong mathematical basis for maximum likelihood optimization of the network parameters via stochastic gradient descent, avoiding the issue of gradient backpropagation through the discontinuity created by the spiking process.
Three machine learning algorithms are developed for stochastic SNNs with a focus on power efficiency, learning efficiency and model adaptability; characteristics that are valuable in resource constrained settings. They are studied in the context of applications where low power learning on the edge is key. All of the learning rules that are derived include only local variables along with a global learning signal, making these algorithms tractable to implementation in current neuromorphic hardware.
First, a stochastic SNN that includes only visible neurons, the simplest case for probabilistic optimization, is considered. A policy gradient reinforcement learning (RL) algorithm is developed in which the stochastic SNN defines the policy, or state-action distribution, of an RL agent. Action choices are sampled directly from the policy by interpreting the outputs of the read-out neurons using a first to spike decision rule. This study highlights the power efficiency of the SNN in terms of spike frequency.
Next, an online meta-learning framework is proposed with the goal of progressively improving the learning efficiency of an SNN over a stream of tasks. In this setting, SNNs including both hidden and visible neurons are considered, posing a more complex maximum likelihood learning problem that is solved using a variational learning method. The meta-learning rule yields a hyperparameter initialization for SNN models that supports fast adaptation of the model to individualized data on edge devices.
Finally, moving away from the supervised learning paradigm, a hybrid adver-sarial training framework for SNNs, termed SpikeGAN, is developed. Rather than optimize for the likelihood of target spike patterns at the SNN outputs, the training is mediated by an auxiliary discriminator that provides a measure of how similar the spiking data is to a target distribution. Because no direct spiking patterns are given, the SNNs considered in adversarial learning include only hidden neurons. A Bayesian adaptation of the SpikeGAN learning rule is developed to broaden the range of temporal data that a single SpikeGAN can estimate. Additionally, the online meta-learning rule is extended to include meta-learning for SpikeGAN, to enable efficient generation of data from sequential data distributions
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