CrossNets: High‐Performance Neuromorphic Architectures for CMOL Circuits

Abstract

The exponential, Moore's Law, progress of electronics may be continued beyond the 10-nm frontier if the currently dominant CMOS technology is replaced by hybrid CMOL circuits combining a silicon MOSFET stack and a few layers of parallel nanowires connected by self-assembled molecular electronic devices. Such hybrids promise unparalleled performance for advanced information processing, but require special architectures to compensate for specific features of the molecular devices, including low voltage gain and possible high fraction of faulty components. Neuromorphic networks with their defect tolerance seem the most natural way to address these problems. Such circuits may be trained to perform advanced information processing including (at least) effective pattern recognition and classification. We are developing a family of distributed crossbar network (CrossNet) architectures that permit the combination of high connectivity neuromorphic circuits with high component density. Preliminary estimates show that this approach may eventually allow us to place a cortex-scale circuit with about 10(10) neurons and about 10(14) synapses on an approximately 10 x 10 cm(2) silicon wafer. Such systems may provide an average cell-to-cell latency of about 20 nsec and, thus, perform information processing and system training (possibly including self-evolution after initial training) at a speed that is approximately six orders of magnitude higher than in its biological prototype and at acceptable power dissipation.

Intrinsic constrains on thermally-assisted memristive switching

“…For example, one can use the formulas to estimate conducting channel dimensions given particular I-V characteristics for unipolar and certain bipolar memristive devices. Likewise, the results can be directly applied to cells in crossbar digital memories and other circuits based on resistive switching devices [14,[20][21][22]. Fig.…”

Stochastic resonance in an ensemble of single-electron neuromorphic devices and its application to competitive neural networks

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Abstract-Neuromorphic computing based on singleelectron circuit technology is gaining prominence because of its massively increased computational efficiency and the increasing relevance of computer technology and nanotechnology [1,2]. The maximum impact of these technologies will be strongly felt when single-electron circuits based on fault-and noise-tolerant neural structures can operate at room temperature.

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CMOL: Second life for silicon?

“…In our recent work [34] we have proved that, despite these limitations, CrossNets can be taught, by at least two different methods, to perform virtually all the major functions demonstrated earlier with usual neural networks, including the corrupted pattern restoration in the recurrent quasi-Hopfield mode and pattern classification in the feedforward MLP mode. The importance of this result is in the CrossNet's potential unparalleled density and speed [11,34]: for realistic parameters, the cell density may exceed that of cerebral cortex (above 10 7 cells per cm 2 ), while the average cell-to-cell communication delay may be as low as ~10 ns (i.e., about six orders of magnitude lower than that in the brain), at acceptable power. Even putting aside the exciting long-term prospects of creating high-speed artificial brain-like systems [34], CMOL CrossNet chips of modest size might be used for important present-day problems, e.g., online recognition of a person in a large crowd [35].…”

“…32). We have explored a specific architecture of such networks, called Distributed Crossbar Networks ("CrossNets") [11,34], which are uniquely suitable for CMOL implementation.…”

“…single-electron transistor Room-temperature versions of such devices may be readily implemented using relatively simple organic molecules [11]. (Their main components, molecular single-electron transistors, have already been demonstrated by several research groups [12][13][14][15][16].…”