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A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
In the second part, we review what is knows about the different new non-volatile memory materials and devices suited for compute in-memory, and discuss the outlook and challenges.
Neural Network Training with Asymmetric Crosspoint Elements
Analog crossbar arrays comprising programmable nonvolatile resistors are under intense investigation for acceleration of deep neural network training.
Algorithm for Training Neural Networks on Resistive Device Arrays
Hardware architectures composed of resistive cross-point device arrays can provide significant power and speed benefits for deep neural network training workloads using stochastic gradient descent (SGD) and backpropagation (BP) algorithm.
Training large-scale ANNs on simulated resistive crossbar arrays
Accelerating training of artificial neural networks (ANN) with analog resistive crossbar arrays is a promising idea.
Efficient ConvNets for Analog Arrays
Analog arrays are a promising upcoming hardware technology with the potential to drastically speed up deep learning.
Training LSTM Networks with Resistive Cross-Point Devices
In our previous work we have shown that resistive cross point devices, so called Resistive Processing Unit (RPU) devices, can provide significant power and speed benefits when training deep fully connected networks as well as convolutional neural networks.
Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices
This concept of Resistive Processing Unit (RPU) devices we extend here towards convolutional neural networks (CNNs).
