Superparamagnetic tunnel junctions (SMTJs) and spin-torque nano-oscillators (STNOs) show promise for use in energy-efficient unconventional computing schemes based on stochastic information encodings, operating from nanosecond to microsecond time scales. We demonstrate electrical coupling of SMTJs for emulating neuro-synaptic connections and leverage the phase dynamics of STNOs for innovative approaches to unbiased random number generation, with the potential to mimic fast stochastic binary neurons, paving the way for low-energy, hardware-based stochastic neural networks.
Due to their interesting physical properties, myriad operational regimes, small size, and industrial fabrication maturity, magnetic tunnel junctions are uniquely suited for unlocking novel computing schemes for in-hardware neuromorphic computing. In this paper, we focus on the stochastic response of magnetic tunnel junctions, illustrating three different ways in which the probabilistic response of a device can be used to achieve useful neuromorphic computing power.
Magnetic tunnel junctions (MTJs) provide an attractive platform for implementing neural networks because of their simplicity, nonvolatility and scalability. In a hardware realization, however, device variations, write errors, and parasitic resistance will generally degrade performance. To quantify such effects, we perform experiments on a 2-layer perceptron constructed from a 15 × 15 passive array of MTJs, examining classification accuracy and write fidelity. Despite imperfections, we achieve accuracy of up to 95.3 % with proper tuning of network parameters. The success of this tuning process shows that new metrics are needed to characterize and optimize networks reproduced in mixed signal hardware.
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