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Stochastic p-bits for Invertible Logic

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TLDR
In this article, a Boltzmann machine (BM) with symmetric connections is designed to implement a given truth table, and it can be interconnected in a partially directed manner to implement large operations such as 32-bit addition.
Abstract
Conventional logic and memory devices are built out of deterministic units such as transistors, or magnets with energy barriers in excess of 40-60 kT. We show that stochastic units, p-bits, can be interconnected to create robust correlations that implement Boolean functions with impressive accuracy, comparable to standard circuits. Also they are invertible, a unique property that is absent in digital circuits. When operated in the direct mode, the input is clamped, and the network provides the correct output. In the inverted mode, the output is clamped, and the network fluctuates among possible inputs consistent with that output. We present an implementation of an invertible gate to bring out the key role of a three-terminal building block to enable the construction of correlated p-bit networks. The results for this implementation agree well with those from a universal model, showing that p-bits need not be magnet-based: any three-terminal tunable random bit generator should be suitable. We present an algorithm for designing a Boltzmann machine (BM) with symmetric connections that implements a given truth table. We then show how BM Full Adders can be interconnected in a partially directed manner to implement large operations such as 32-bit addition. Hundreds of p-bits get precisely correlated such that the correct answer out of 2^33 possibilities can be extracted by looking at the mode of a number of time samples. With perfect directivity a small number of samples is enough, while for less directed connections more samples are needed, but even in the former case invertibility is largely preserved. This combination of accuracy and invertibility is enabled by the hybrid design that uses bidirectional units to construct circuits with partially directed connections. We establish this result with examples including a 4-bit multiplier which in inverted mode functions as a factorizer.

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Citations
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Ising machines as hardware solvers of combinatorial optimization problems

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References
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Simulating physics with computers

TL;DR: In this paper, the authors describe the possibility of simulating physics in the classical approximation, a thing which is usually described by local differential equations, and the possibility that there is to be an exact simulation, that the computer will do exactly the same as nature.
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A learning algorithm for boltzmann machines

TL;DR: A general parallel search method is described, based on statistical mechanics, and it is shown how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way.
Journal ArticleDOI

Spin-torque switching with the giant spin hall effect of tantalum

TL;DR: In this paper, a giant spin Hall effect (SHE) in β-tantalum was shown to generate spin currents intense enough to induce spin-torque switching of ferromagnets at room temperature.
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Memristive devices for computing

TL;DR: The performance requirements for computing with memristive devices are examined and how the outstanding challenges could be met are examined.