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Found 18 papers, 4 papers with code
Benchmarking Spiking Neural Network Learning Methods with Varying Locality
However, training SNNs is challenging due to the non-differentiable nature of the spiking mechanism.
Astrocyte Regulated Neuromorphic Central Pattern Generator Control of Legged Robotic Locomotion
Neuromorphic computing systems, where information is transmitted through action potentials in a bio-plausible fashion, is gaining increasing interest due to its promise of low-power event-driven computing.
Delving Deeper Into Astromorphic Transformers
Preliminary attempts at incorporating the critical role of astrocytes - cells that constitute more than 50% of human brain cells - in brain-inspired neuromorphic computing remain in infancy.
SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation
Moreover, the convergence of average spiking rate of neurons at equilibrium is utilized to develop a novel ANN-SNN knowledge distillation based technique wherein we use a pre-trained BERT model as "teacher" to train our "student" spiking architecture.
Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity
Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning mechanism for Spiking Neural Networks (SNNs) that has received significant attention from the neuromorphic hardware community.
Astromorphic Self-Repair of Neuromorphic Hardware Systems
While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention is still focused on computational units like the neuron and synapse.
Sequence Learning Using Equilibrium Propagation
However, by definition, EP requires the input to the model (a convergent RNN) to be static in both the phases of training.
On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs
Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in the algorithm and hardware design of next-generation machine learning platforms.
Power System Disturbance Classification with Online Event-Driven Neuromorphic Computing
Accurate online classification of disturbance events in a transmission network is an important part of wide-area monitoring.
Exploring the Connection Between Binary and Spiking Neural Networks
On-chip edge intelligence has necessitated the exploration of algorithmic techniques to reduce the compute requirements of current machine learning frameworks.
Exploiting Oxide Based Resistive RAM Variability for Bayesian Neural Network Hardware Design
Uncertainty plays a key role in real-time machine learning.
All-Spin Bayesian Neural Networks
Probabilistic machine learning enabled by the Bayesian formulation has recently gained significant attention in the domain of automated reasoning and decision-making.
Emerging Technologies
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
We present RxNN, a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar systems.
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware.
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design
Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks.
RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks
In this paper, we propose RESPARC - a reconfigurable and energy efficient architecture built-on Memristive Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs).
Energy-Efficient Object Detection using Semantic Decomposition
We propose a 2-stage hierarchical classification framework, with increasing levels of complexity, wherein the first stage is trained to recognize the broad representative semantic features relevant to the object of interest.
Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications.
