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Neural-like computing with populations of superparamagnetic basis functions
- Publication Year :
- 2016
-
Abstract
- In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for calculating. For this purpose, the responses of the nanodevices should be non-linear, and each tuned to different values of the input. These strong requirements have prevented a demonstration of population coding with nanodevices. Here, we show that nanoscale magnetic tunnel junctions can be assembled to meet these requirements. We demonstrate experimentally that a population of nine junctions can implement a basis set of functions, providing the data to achieve, for example, the generation of cursive letters. We design hybrid magnetic-CMOS systems based on interlinked populations of junctions and show that they can learn to realize non-linear variability-resilient transformations with a low imprint area and low power.<br />Comment: 5 figures
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1610.09394
- Document Type :
- Working Paper