Search Results for author:
Found 18 papers, 11 papers with code
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
Despite a well-established mathematical foundation for neural dynamics, the implementation details vary greatly across different platforms.
To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration
The power efficiency of the biological brain outperforms any large-scale deep learning ( DL ) model; thus, neuromorphic computing tries to mimic the brain operations, such as spike-based information processing, to improve the efficiency of DL models.
Memristive Reservoirs Learn to Learn
Using the framework, we successfully identify the optimal hyperparameters for the reservoir.
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.
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
As a result, their performance lags behind modern deep learning, and we are yet to see the effectiveness of SNNs in language generation.
OpenSpike: An OpenRAM SNN Accelerator
This paper presents a spiking neural network (SNN) accelerator made using fully open-source EDA tools, process design kit (PDK), and memory macros synthesized using OpenRAM.
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.
Spiking neural networks for nonlinear regression
To overcome this problem, a framework for regression using spiking neural networks is proposed.
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).
SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks
The natural spiking dynamics of the MIF neuron model are fully differentiable, eliminating the need for gradient approximations that are prevalent in the spiking neural network literature.
A Fully Memristive Spiking Neural Network with Unsupervised Learning
We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule.
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.
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.
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.
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.
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge.
Adaptive Precision CNN Accelerator Using Radix-X Parallel Connected Memristor Crossbars
This novel algorithm-hardware solution is described as the radix-X Convolutional Neural Network Crossbar Array, and demonstrate how to efficiently represent negative weights using a single column line, rather than double the number of additional columns.
