Autonomous Probabilistic Coprocessing With Petaflips per Second

IEEE Access

Rafatul Faria¹  Supriyo Datta¹  Risi Jaiswal¹  Brian Sutton¹  Lakshmi Anirudh Ghantasala¹  Kerem Yunus Camsari² 

[1] Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA;Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA, USA;

关键词: Probabilistic logic;    Boltzmann machines;    Ising machines;    neural network hardware;    combinatorial optimization;    quantum Monte Carlo;   

DOI  :  10.1109/ACCESS.2020.3018682

来源: DOAJ

【 摘 要 】

In this article we present a concrete design for a probabilistic (p-) computer based on a network of p-bits, robust classical entities fluctuating between -1 and +1, with probabilities that are controlled through an input constructed from the outputs of other p-bits. The architecture of this probabilistic computer is similar to a stochastic neural network with the p-bit playing the role of a binary stochastic neuron, but with one key difference: there is no sequencer used to enforce an ordering of p-bit updates, as is typically required. Instead, we explore sequencerless designs where all p-bits are allowed to flip autonomously and demonstrate that such designs can allow ultrafast operation unconstrained by available clock speeds without compromising the solution's fidelity. Based on experimental results from a hardware benchmark of the autonomous design and benchmarked device models, we project that a nanomagnetic implementation can scale to achieve petaflips per second with millions of neurons. A key contribution of this article is the focus on a hardware metric - flips per second - as a problem and substrate-independent figure-of-merit for an emerging class of hardware annealers known as Ising Machines. Much like the shrinking feature sizes of transistors that have continually driven Moore's Law, we believe that flips per second can be continually improved in later technology generations of a wide class of probabilistic, domain specific hardware.

【 授权许可】

Unknown