Spintronic Devices as Key Elements for Energy-Efficient Neuroinspired Architectures
Nicolas Locatelli1,a, Adrien F. Vincent1, Alice Mizrahi1,3, Joseph S. Friedman1, Damir Vodenicarevic1, Joo-Von Kim1, Jacques-Olivier Klein1, Weisheng Zhao1,2, Julie Grollier3 and Damien Querlioz1,b
1Institut d’Electronique Fondamentale, Univ. Paris-Sud, CNRS, Orsay, France.
2Spintronics Interdisciplinary Center, Beihang University, Beijing, China
3Unite Mixte de Physique CNRS/Thales and Universite Paris-Sud, 1 Ave. A. Fresnel, Palaiseau, France
Processing the current deluge of data using conventional CMOS architectures requires a tremendous amount of energy, as it is inefficient for tasks such as data mining, recognition and synthesis. Alternative models of computation based on neuroinspiration can prove much more efficient for these kinds of tasks, but do not map ideally to traditional CMOS. Spintronics, by contrast, can bring features such as embedded nonvolatile memory and stochastic and memristive behavior, which, when associated with CMOS, can be key enablers for neuroinspired computing. In this paper, we explore different works that go in this direction. First, we illustrate how recent developments in embedded nonvolatile memory based on magnetic tunnel junctions (MTJs) can provide the large amount of nonvolatile memory required in neuro-inspired designs while avoiding Von Neumann bottleneck. Second, we show that recently developed spintronic memristors can implement artificial synapses for neuromorphic systems. With a more groundbreaking design, we show how the probabilistic writing of single MTJ bits can efficiently replace multi-level weighting for some classes of neuroinspired architectures. Finally, we show that a special class of MTJs can exhibit the phenomenon of stochastic resonance, a strategy used in biological systems to detect weak signals. These results suggest that the impact of spintronics extends beyond the traditional standalone and embedded memory markets.
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