NDLI: Spintronic devices as key elements for energy-efficient neuroinspired architectures

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Spintronic devices as key elements for energy-efficient neuroinspired architectures

Content Provider	ACM Digital Library
Author	Zhao, Weisheng Kim, Joo-Von Grollier, Julie Locatelli, Nicolas Querlioz, Damien Friedman, Joseph S. Mizrahi, Alice Vodenicarevic, Damir Klein, Jacques-Olivier Vincent, Adrien F.
Abstract	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.
Starting Page	994
Ending Page	999
Page Count	6
File Format	PDF
ISBN	9783981537048
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-09
Publisher Place	San Jose
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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