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Exploiting Non-idealities of Resistive Switching Memories for Efficient Machine Learning
2022
Frontiers in Electronics
Novel computing architectures based on resistive switching memories (also known as memristors or RRAMs) have been shown to be promising approaches for tackling the energy inefficiency of deep learning and spiking neural networks. However, resistive switch technology is immature and suffers from numerous imperfections, which are often considered limitations on implementations of artificial neural networks. Nevertheless, a reasonable amount of variability can be harnessed to implement efficient
doi:10.3389/felec.2022.825077
fatcat:tian3vabpfbnlpuukvqobq2yzq