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Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives
Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications.
Efficient Training of Spiking Neural Networks with Temporally-Truncated Local Backpropagation through Time
The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on dynamic-vision-sensor (DVS) recorded datasets.
Learning in Memristive Neural Network Architectures using Analog Backpropagation Circuits
The circuit level design and implementation of backpropagation algorithm using gradient descent operation for neural network architectures is an open problem.
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