Robustness of binary stochastic neurons implemented with low barrier nanomagnets made of dilute magnetic semiconductors

Abstract

Binary stochastic neurons (BSNs) are excellent hardware accelerators for machine learning. A popular platform for implementing them are low- or zero-energy barrier nanomagnets possessing in-plane magnetic anisotropy (e.g. circular disks or quasi-elliptical disks with very small eccentricity). Unfortunately, small geometric variations in the lateral shapes of such nanomagnets can produce large changes in the BSN response times if the nanomagnets are made of common metallic ferromagnets (Co, Ni, Fe) with large saturation magnetization. Additionally, the response times are also very sensitive to initial conditions. Here, we show that if the nanomagnets are made of dilute magnetic semiconductors with much smaller saturation magnetization, then the variability in their response times (due to shape variations and variation in the initial condition) is drastically suppressed. This significantly reduces the device-to-device variation, which is a serious challenge for large scale neuromorphic systems.

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• Quantitative Evaluation of Hardware Binary Stochastic Neurons
• An observable effect of spin inertia in slow magneto-dynamics: Increase of the switching error rates in nanoscale ferromagnets

Cited by in corpus (3)

• A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
• Roadmap for Unconventional Computing with Nanotechnology
• The Strong Sensitivity of the Characteristics of Binary Stochastic Neurons Employing Low Barrier Nanomagnets to Small Geometrical Variations