arrow
Return

Correlation free large-scale probabilistic computing using a true-random chaotic oscillator p-bit

delete2025-03-07
delete0
delete
OA
AI
W
Woojin Lee
H
Hyunjin Kim
H
Hyundo Jung
Y
Yohan Choi
J
Jin-Woo Jeon
C
Chulwoo Kim *
DOI:10.1038/s41598-025-93218-8delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
Probabilistic computing-quantum-inspired computing that uses probabilistic bits (p-bits)-has emerged as a powerful method owing to its fast search speed and robust connectivity. Previous works used linear feedback shift registers (LFSRs) or stochastic magnetic tunnel junctions (MTJs) to implement p-bits. However, in large-scale problems, periodicity and correlation issues in LFSR p-bits and inherent variations in MTJ-based p-bits with narrow stochastic regions lead to unreliable results when seeking the appropriate solution. Therefore, we propose a fully CMOS frequency-scalable p-bit implemented with a discrete-time flipped-hook tent-map chaotic oscillator. The proposed chaotic oscillator produces high-quality noise voltage that is uniformly distributed across the entire supply voltage range, enabling aligned responses of p-bits free from calibration and an input resolution of 8 bits. In contrast to LFSR-based p-bits with hardware-dependent correlation, the chaotic oscillator p-bits could factorize semiprimes with lengths up to 64 bits without changing hardware size. The chaotic oscillator exhibited an energy efficiency of 4.26 pJ/bit at 1.8 V supply voltage. The robustness and the high randomness of the proposed chaotic oscillator p-bit suggest a new direction of a p-bit scalable to large-scale probabilistic computing.
Keywords:
RANDOM NUMBER GENERATORS
IMPLEMENTATION
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Scientific Reports cover
Scientific Reports
IF:
3.9
Papers:
27.4W
Citations:
83.5W

Organization

K
Korea University
Scholars:
3.6W
Papers: 3.8W
Citations: 4.4W