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Found 8 papers, 3 papers with code
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).
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
The brain is the perfect place to look for inspiration to develop more efficient neural networks.
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.
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.
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.
