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Title: Semi-supervised learning and inference in domain-wall magnetic tunnel junction (DW-MTJ) neural networks
Advances in machine intelligence have sparked interest in hardware accelerators to implement these algorithms, yet embedded electronics have stringent power, area budgets, and speed requirements that may limit nonvolatile memory (NVM) integration. In this context, the development of fast nanomagnetic neural networks using minimal training data is attractive. Here, we extend an inference-only proposal using the intrinsic physics of domain-wall MTJ (DW-MTJ) neurons for online learning to implement fully unsupervised pattern recognition operation, using winner-take-all networks that contain either random or plastic synapses (weights). Meanwhile, a read-out layer trains in a supervised fashion. We find our proposed design can approach state-of-the-art success on the task relative to competing memristive neural network proposals, while eliminating much of the area and energy overhead that would typically be required to build the neuronal layers with CMOS devices.  more » « less
Award ID(s):
1910800
NSF-PAR ID:
10145308
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
SPIE Spintronics XII
Volume:
11090
Page Range / eLocation ID:
110903I
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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