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 computing. Such a population can be implemented with CMOS technology, but the corresponding circuits have high area or energy requirements. Here, we show that nanoscale magnetic tunnel junctions can instead 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.
Highlights
In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing
We show that a nanodevice—the superparamagnetic tunnel junction—naturally implements neurons for population coding, and that it can be exploited for designing systems that can compute and learn
In this work, we show that superparamagnetic tunnel junctions are promising nanodevices for computing in hardware through population coding
Summary
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 computing Such a population can be implemented with CMOS technology, but the corresponding circuits have high area or energy requirements. It is attractive to take inspiration from this strategy and compute with populations of low-area nanoscale electronic devices, even when they exhibit stochastic or variable behaviors This approach has recently inspired pioneering studies of the dynamical response of ensembles of emerging nanodevices[14,15]. The ensemble of tuning curves in the population forms a basis set of functions (bottom panel of Fig. 1e), similar to the sines and cosines of a Fourier expansion[10,19]
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