Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealing

Onizawa, Naoya; Kuroki, Kyo; Shin, Duckgyu; Hanyu, Takahiro

doi:10.1109/ojsp.2023.3329756

Cited by 3 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hyperparameters for the simulated annealing processes, such as I 0min , I 0max , and β , are not arbitrarily selected. Rather, they are determined in accordance with a specific statistical method, which is designed to optimize the performance of the simulated annealing algorithm (SSA) 44 . In addition to these, a traditional SA algorithm, a well-established method for optimization problems, is also implemented for the sake of performance comparison 16 .…”

Section: Algorithm Equationsmentioning

confidence: 99%

Enhanced convergence in p-bit based simulated annealing with partial deactivation for large-scale combinatorial optimization problems

Onizawa,

Hanyu

2024

Sci Rep

View full text Add to dashboard Cite

This article critically investigates the limitations of the simulated annealing algorithm using probabilistic bits (pSA) in solving large-scale combinatorial optimization problems. The study begins with an in-depth analysis of the pSA process, focusing on the issues resulting from unexpected oscillations among p-bits. These oscillations hinder the energy reduction of the Ising model and thus obstruct the successful execution of pSA in complex tasks. Through detailed simulations, we unravel the root cause of this energy stagnation, identifying the feedback mechanism inherent to the pSA operation as the primary contributor to these disruptive oscillations. To address this challenge, we propose two novel algorithms, time average pSA (TApSA) and stalled pSA (SpSA). These algorithms are designed based on partial deactivation of p-bits and are thoroughly tested using Python simulations on maximum cut benchmarks that are typical combinatorial optimization problems. On the 16 benchmarks from 800 to 5000 nodes, the proposed methods improve the normalized cut value from 0.8 to 98.4% on average in comparison with the conventional pSA.

show abstract

Section: Algorithm Equationsmentioning

confidence: 99%

Enhanced convergence in p-bit based simulated annealing with partial deactivation for large-scale combinatorial optimization problems

Onizawa,

Hanyu

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The hyperparameters for the simulated annealing processes, such as I 0min , I 0max , and β , are not arbitrarily selected. Rather, they are determined in accordance with a specific statistical method, which is designed to optimize the performance of the simulated annealing algorithm (SSA) 33 . In addition to these, a traditional SA algorithm, a well-established method for optimization problems, is also implemented for the sake of performance comparison 11 .…”

Section: Max-cut Problems and Annealing Parameters For Evaluatoinmentioning

confidence: 99%

Enhanced Convergence in p-bit Based Simulated Annealing with Partial Deactivation for Large-Scale Combinatorial Optimization Problems

Onizawa,

Hanyu

2023

Preprint

Self Cite

View full text Add to dashboard Cite

This article critically investigates the limitations of the simulated annealing algorithm using probabilistic bits (pSA) in solving large-scale combinatorial optimization problems. The study begins with an in-depth analysis of the pSA process, focusing on the issues resulting from unexpected oscillations among p-bits. These oscillations hinder the energy reduction of the Ising model and thus obstruct the successful execution of pSA in complex tasks. Through detailed simulations, we unravel the root cause of this energy stagnation, identifying the feedback mechanism inherent to the pSA operation as the primary contributor to these disruptive oscillations. To address this challenge, we propose two novel algorithms, time average pSA (TApSA) and stalled pSA (SpSA). These algorithms are designed based on partial deactivation of p-bits and are thoroughly tested using Python simulations on maximum cut benchmarks that are typical combinatorial optimization problems. On the 16 benchmarks from 800 to 5,000 nodes, the proposed methods improve the normalized cut value from 0.8% to 98.4% on average in comparison with the conventional pSA.

show abstract

Ferroelectric compute-in-memory annealer for combinatorial optimization problems

Yin,

Qian,

Vardar

et al. 2024

Nat Commun

View full text Add to dashboard Cite

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications. Various digital annealers, dynamical Ising machines, and quantum/photonic systems have been developed for solving COPs, but they still suffer from the memory access issue, scalability, restricted applicability to certain types of COPs, and VLSI-incompatibility, respectively. Here we report a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer for solving larger-scale COPs efficiently. Our CiM annealer converts COPs into quadratic unconstrained binary optimization (QUBO) formulations, and uniquely accelerates in-situ the core vector-matrix-vector (VMV) multiplication operations of QUBO formulations in a single step. Specifically, the three-terminal FeFET structure allows for lossless compression of the stored QUBO matrix, achieving a remarkably 75% chip size saving when solving Max-Cut problems. A multi-epoch simulated annealing (MESA) algorithm is proposed for efficient annealing, achieving up to 27% better solution and ~ 2X speedup than conventional simulated annealing. Experimental validation is performed using the first integrated FeFET chip on 28nm HKMG CMOS technology, indicating great promise of FeFET CiM array in solving general COPs.

show abstract

Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealing

Cited by 3 publications

References 29 publications

Enhanced convergence in p-bit based simulated annealing with partial deactivation for large-scale combinatorial optimization problems

Enhanced convergence in p-bit based simulated annealing with partial deactivation for large-scale combinatorial optimization problems

Enhanced Convergence in p-bit Based Simulated Annealing with Partial Deactivation for Large-Scale Combinatorial Optimization Problems

Ferroelectric compute-in-memory annealer for combinatorial optimization problems

Contact Info

Product

Resources

About