19 citations · 45 across the 5 of their papers we have counts for
5 papers
Fast Solving Complete 2000-Node Optimization Using Stochastic-Computing Simulated Annealing
Kota Katsuki, Duckgyu Shin, Naoya Onizawa +1
In this paper, we evaluate stochastic-computing simulated annealing (SC-SA) for solving large-scale combinatorial optimization problems. SC-SA is designed using stochastic computin…
Energy-Efficient p-Bit-Based Fully-Connected Quantum-Inspired Simulated Annealer with Dual BRAM Architecture
Naoya Onizawa, Taiga Kubuta, Duckgyu Shin +1
Probabilistic bits (p-bits) offer an energy-efficient hardware abstraction for stochastic optimization; however, existing p-bit-based simulated annealing accelerators suffer from p…
Memory-Efficient FPGA Implementation of Stochastic Simulated Annealing
Duckgyu Shin, Naoya Onizawa, Warren J. Gross +1
Simulated annealing (SA) is a well-known algorithm for solving combinatorial optimization problems. However, the computation time of SA increases rapidly, as the size of the proble…
Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealing
Naoya Onizawa, Kyo Kuroki, Duckgyu Shin +1
This paper presents a local energy distribution based hyperparameter determination for stochastic simulated annealing (SSA). SSA is capable of solving combinatorial optimization pr…
Stochastic Simulated Quantum Annealing for Fast Solution of Combinatorial Optimization Problems
Naoya Onizawa, Ryoma Sasaki, Duckgyu Shin +2
In this paper, we introduce stochastic simulated quantum annealing (SSQA) for large-scale combinatorial optimization problems. SSQA is designed based on stochastic computing and qu…