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Found 12 papers, 4 papers with code
Training Spiking Neural Networks Using Lessons From Deep Learning
This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks.
Navigating Local Minima in Quantized Spiking Neural Networks
Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms.
The fine line between dead neurons and sparsity in binarized spiking neural networks
Spiking neural networks can compensate for quantization error by encoding information either in the temporal domain, or by processing discretized quantities in hidden states of higher precision.
Intelligence Processing Units Accelerate Neuromorphic Learning
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads.
Field-Programmable Crossbar Array (FPCA) for Reconfigurable Computing
The proposed computing architecture is based on a uniform, physical, resistive, memory-centric fabric that can be optimally reconfigured and utilized to perform different computing and data storage tasks in a massively parallel approach.
Memristive Stochastic Computing for Deep Learning Parameter Optimization
Stochastic Computing (SC) is a computing paradigm that allows for the low-cost and low-power computation of various arithmetic operations using stochastic bit streams and digital logic.
Hierarchical Architectures in Reservoir Computing Systems
Analogous to deep neural networks, stacking sub-reservoirs in series is an efficient way to enhance the nonlinearity of data transformation to high-dimensional space and expand the diversity of temporal information captured by the reservoir.
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures
The impact of device and circuit-level effects in mixed-signal Resistive Random Access Memory (RRAM) accelerators typically manifest as performance degradation of Deep Learning (DL) algorithms, but the degree of impact varies based on algorithmic features.
Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays
We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs).
RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Network
Event-based cameras are inspired by the sparse and asynchronous spike representation of the biological visual system.
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput.
Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural Network Training
The efficacy of training larger models is evaluated using realistic hardware parameters and shows that that analog CIM modules can enable efficient mix-precision DNN training with accuracy comparable to full-precision software trained models.
