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applsciletetrsa>vol-01>issue-05>Physics-inspired Ising Computing with Ring Oscillator Activated p-bits

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Letter

Physics-inspired Ising Computing with Ring Oscillator Activated p-bits

Navid Anjum Aadit
Andrea Grimaldiy

Applied Science Letters

2022 ° 09(06) ° 01-04

https://www.wikipt.org/applscilettersa

DOI: 10.1490/665877402.570applsci

Abstract

The nearing end of Moore’s Law has been driving the development of domain-specific hardware tailored to solve a special set of problems. Along these lines, probabilistic computing with inherently stochastic building blocks (p-bits) have shown significant promise, particularly in the context of hard optimization and statistical sampling problems. p-bits have been proposed and demonstrated in different hardware substrates ranging from small-scale stochastic magnetic tunnel junctions (sMTJs) in asynchronous architectures to large-scale CMOS in synchronous architectures. Here, we design and implement a truly asynchronous and medium-scale p-computer (with  800 pbits) that closely emulates the asynchronous dynamics of sMTJs in Field Programmable Gate Arrays (FPGAs). Using hard instances of the planted Ising glass problem on the Chimera lattice, we evaluate the performance of the asynchronous architecture against an ideal, synchronous design that performs parallelized (chromatic) exact Gibbs sampling. We find that despite the lack of any careful synchronization, the asynchronous design achieves parallelism with comparable algorithmic scaling in the ideal, carefully tuned and parallelized synchronous design. Our results highlight the promise of massively scaled p-computers with millions of free-running p-bits made out of nanoscale building blocks such as stochastic magnetic tunnel junctions.

Introduction


With the nearing end of Moore’s Law, domain-specific hardware and architectures are growing rapidly. The notion of performing some tasks more efficiently (area, speed and/or energy) rather than improving performance for general purpose computing has led to the proliferation of special-purpose accelerators. With their widespread use, hard optimization problems have been a primary target of this approach and a variety of different domain-specific hardware architectures have emerged (see, Ref. [1] for a general and recent review). As an example of this growing trend, probabilistic bits or p-bits were introduced [2] as a building block which can accelerate a broad family of algorithms including Monte Carlo, Markov Chain Monte Carlo [3], Quantum Monte Carlo, statistical sampling for Bayesian inference and Boltzmann machine learning [4] methods. p-bits have been shown to be compatible with powerful optimization techniques such asparallel tempering [5] with competitive performance relative to all other Ising machines (classical and quantum) in select problems such as integer factorization and Boolean satisfiability [6]. Their combination with sophisticated algorithms [7] could yield further advantages. A natural advantage of the p-bit model is its native mapping to the Ising model and to the natural generalization of Ising Models. This ensures that coupled p-bits can systematically probe the exact Boltzmann distribution through Gibbs or Metropolis sampling without any approximations or reductions.

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