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Found 32 papers, 2 papers with code
Efficient Deployment of Transformer Models in Analog In-Memory Computing Hardware
Moreover, it supports on-chip adaptation to new hardware constraints and tasks without updating analog weights, providing a flexible and versatile solution for real-world AI applications.
Neuromorphic Wireless Split Computing with Multi-Level Spikes
Inspired by biological processes, neuromorphic computing utilizes spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data.
In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models
Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions.
Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
In this paper, we develop a hardware-software co-optimisation strategy to port software-trained deep neural networks (DNN) to reduced-precision spiking models demonstrating fast and accurate inference in a novel event-driven CMOS reconfigurable spiking inference accelerator.
Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning
Robustness to artefacts was assessed by corrupting ECG data with sinusoidal baseline drift, shift, rescaling and noise, before encoding.
Neuromorphic In-Context Learning for Energy-Efficient MIMO Symbol Detection
In-context learning (ICL), a property demonstrated by transformer-based sequence models, refers to the automatic inference of an input-output mapping based on examples of the mapping provided as context.
Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency.
Bayesian Inference Accelerator for Spiking Neural Networks
Bayesian neural networks offer better estimates of model uncertainty compared to frequentist networks.
Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications
Spiking neural networks (SNNs) implemented on neuromorphic processors (NPs) can enhance the energy efficiency of deployments of artificial intelligence (AI) for specific workloads.
Noise Adaptor in Spiking Neural Networks
Our research utilizes the ResNet model for a comprehensive analysis of the impact of the noise adaptor on low-latency SNNs.
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.
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.
A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces
Brain-computer interfaces are being explored for a wide variety of therapeutic applications.
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems.
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning
Accordingly, a central problem in neuromorphic computing is training spiking neural networks (SNNs) to reproduce spatio-temporal spiking patterns in response to given spiking stimuli.
Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning
Spiking Neural Networks (SNNs) have recently gained popularity as machine learning models for on-device edge intelligence for applications such as mobile healthcare management and natural language processing due to their low power profile.
Hybrid In-memory Computing Architecture for the Training of Deep Neural Networks
We propose a hybrid in-memory computing (HIC) architecture for the training of DNNs on hardware accelerators that results in memory-efficient inference and outperforms baseline software accuracy in benchmark tasks.
SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks
We discuss a high-performance and high-throughput hardware accelerator for probabilistic Spiking Neural Networks (SNNs) based on Generalized Linear Model (GLM) neurons, that uses binary STT-RAM devices as synapses and digital CMOS logic for neurons.
Human Activity Recognition
Vocal Bursts Intensity Prediction
Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence.
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Strategies to improve the efficiency of MVM computation in hardware have been demonstrated with minimal impact on training accuracy.
Accurate deep neural network inference using computational phase-change memory
In-memory computing is a promising non-von Neumann approach where certain computational tasks are performed within memory units by exploiting the physical attributes of memory devices.
Emerging Technologies
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems.
Low-Power Neuromorphic Hardware for Signal Processing Applications
In this paper, we review some of the architectural and system level design aspects involved in developing a new class of brain-inspired information processing engines that mimic the time-based information encoding and processing aspects of the brain.
Training Multi-layer Spiking Neural Networks using NormAD based Spatio-Temporal Error Backpropagation
To tackle this, first the problem of training a multi-layer SNN is formulated as an optimization problem such that its objective function is based on the deviation in membrane potential rather than the spike arrival instants.
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients
In this work, the use of SNNs as stochastic policies is explored under an energy-efficient first-to-spike action rule, whereby the action taken by the RL agent is determined by the occurrence of the first spike among the output neurons.
Adversarial Training for Probabilistic Spiking Neural Networks
Due to the prominence of Artificial Neural Networks (ANNs) as classifiers, their sensitivity to adversarial examples, as well as robust training schemes, have been recently the subject of intense investigation.
Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks
On the standard MNIST database images of handwritten digits, our network achieves an accuracy of 99. 80% on the training set and 98. 06% on the test set, with nearly 7x fewer parameters compared to the state-of-the-art spiking networks.
Stochastic Deep Learning in Memristive Networks
We also study the performance of stochastic memristive DNNs when used as inference engines with noise corrupted data and find that if the device variability can be minimized, the relative degradation in performance for the Stochastic DNN is better than that of the software baseline.
Training Probabilistic Spiking Neural Networks with First-to-spike Decoding
Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes.
Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats
We demonstrate a spiking neural circuit for azimuth angle detection inspired by the echolocation circuits of the Horseshoe bat Rhinolophus ferrumequinum and utilize it to devise a model for navigation and target tracking, capturing several key aspects of information transmission in biology.
Sub-threshold CMOS Spiking Neuron Circuit Design for Navigation Inspired by C. elegans Chemotaxis
We demonstrate a spiking neural network for navigation motivated by the chemotaxis network of Caenorhabditis elegans.
A neural circuit for navigation inspired by C. elegans Chemotaxis
In order to harness the computational advantages spiking neural networks promise over their non-spiking counterparts, we develop a network comprising 7-spiking neurons with non-plastic synapses which we show is extremely robust in tracking a range of concentrations.
