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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
[article]
2019
arXiv
pre-print
On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential. While algorithmic advances in neuromorphic computing are proceeding successfully, the potential of memristors to improve neuromorphic computing have not yet born fruit, primarily because they are often used as a drop-in replacement to conventional memory. However, interdisciplinary
arXiv:1909.01771v2
fatcat:gz4ndpuzi5hytha5572bum3o4y