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• W3083519740 abstract "Energy-efficient methods are addressed for lever-aging low energy barrier nanomagnetic devices within neuro-morphic architectures. Using a Magnetoresistive Random Access Memory (MRAM) probabilistic device (p-bit) as the basis of neuronal structures in Deep Belief Networks (DBNs), the impact of increasing the Magnetic Tunnel Junction's (MTJ's) energy barrier is assessed and optimized for the resulting stochasticity present in the learning system. A self-compensating circuit is developed herein providing a compact, and low complexity approach to mitigating process variation impacts towards practical implementation and fabrication. As evaluated for the MNIST dataset for energy barriers (E B ) at near-zero kT to 2.0 kT, it is shown that the proposed variation-tolerant circuit can effectively increase the reduced probabilistic fluctuation speed of the nanomagnet in p-bits with E B > OkT achieving approximately 5x improvement in the total energy consumption of a 784 × 200 × 10 DBN." @default.
• W3083519740 created "2020-09-11" @default.
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• W3083519740 date "2020-08-01" @default.
• W3083519740 modified "2023-09-27" @default.
• W3083519740 title "Electrically-Tunable Stochasticity for Spin-based Neuromorphic Circuits: Self-Adjusting to Variation" @default.
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• W3083519740 doi "https://doi.org/10.1109/mwscas48704.2020.9184556" @default.
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