Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications

Human activity recognition (HAR) is a classification problem involving time-dependent signals produced by body monitoring, and its application domain covers all the aspects of human life, from healthcare to sport, from safety to smart environments. As such, it is naturally well suited for on-edge deployment of personalized point-of-care (POC) analyses or other tailored services for the user. However, typical smart and wearable devices suffer from relevant limitations regarding energy consumption, and this significantly hinders the possibility for successful employment of edge computing for tasks like HAR. In this paper, we investigate how this problem can be mitigated by adopting a neuromorphic approach. By comparing optimized classifiers based on traditional deep neural network (DNN) architectures as well as on recent alternatives like the Legendre Memory Unit (LMU), we show how spiking neural networks (SNNs) can effectively deal with the temporal signals typical of HAR providing high performances at a low energy cost. By carrying out an application-oriented hyperparameter optimization, we also propose a methodology flexible to be extended to different domains, to enlarge the field of neuro-inspired classifier suitable for on-edge artificial intelligence of things (AIoT) applications

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

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

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.Comment: Manuscript accepted at ICONS'2

Energy-efficient Knowledge Distillation for Spiking Neural Networks

Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation. Considering the future deployment of SNN models to constrained neuromorphic devices, many studies have applied techniques originally used for ANN model compression, such as network quantization, pruning, and knowledge distillation, to SNNs. Among them, existing works on knowledge distillation reported accuracy improvements of student SNN model. However, analysis on energy efficiency, which is also an important feature of SNN, was absent. In this paper, we thoroughly analyze the performance of the distilled SNN model in terms of accuracy and energy efficiency. In the process, we observe a substantial increase in the number of spikes, leading to energy inefficiency, when using the conventional knowledge distillation methods. Based on this analysis, to achieve energy efficiency, we propose a novel knowledge distillation method with heterogeneous temperature parameters. We evaluate our method on two different datasets and show that the resulting SNN student satisfies both accuracy improvement and reduction of the number of spikes. On MNIST dataset, our proposed student SNN achieves up to 0.09% higher accuracy and produces 65% less spikes compared to the student SNN trained with conventional knowledge distillation method. We also compare the results with other SNN compression techniques and training methods

Learning Long Sequences in Spiking Neural Networks

Spiking neural networks (SNNs) take inspiration from the brain to enable energy-efficient computations. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on modern sequential tasks, as they inherit limitations from recurrent neural networks (RNNs), with the added challenge of training with non-differentiable binary spiking activations. However, a recent renewed interest in efficient alternatives to Transformers has given rise to state-of-the-art recurrent architectures named state space models (SSMs). This work systematically investigates, for the first time, the intersection of state-of-the-art SSMs with SNNs for long-range sequence modelling. Results suggest that SSM-based SNNs can outperform the Transformer on all tasks of a well-established long-range sequence modelling benchmark. It is also shown that SSM-based SNNs can outperform current state-of-the-art SNNs with fewer parameters on sequential image classification. Finally, a novel feature mixing layer is introduced, improving SNN accuracy while challenging assumptions about the role of binary activations in SNNs. This work paves the way for deploying powerful SSM-based architectures, such as large language models, to neuromorphic hardware for energy-efficient long-range sequence modelling.Comment: 18 pages, 10 Figures/Table

NeuroBench:A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community