260 citations · 415 across the 9 of their papers we have counts for
9 papers
Efficient CMOS Invertible Logic Using Stochastic Computing
Sean C. Smithson, Naoya Onizawa, Brett H. Meyer +2
Invertible logic can operate in one of two modes: 1) a forward mode, in which inputs are presented and a single, correct output is produced, and 2) a reverse mode, in which the out…
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…
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…
High Convergence Rates of CMOS Invertible Logic Circuits Based on Many-Body Hamiltonians
Naoya Onizawa, Takahiro Hanyu
This paper introduces CMOS invertible-logic (CIL) circuits based on many-body Hamiltonians. CIL can realize probabilistic forward and backward operations of a function by annealing…
Analog-to-Stochastic Converter Using Magnetic Tunnel Junction Devices for Vision Chips
Naoya Onizawa, Daisaku Katagiri, Warren J. Gross +1
This paper introduces an analog-to-stochastic converter using a magnetic tunnel junction (MTJ) device for vision chips based on stochastic computation. Stochastic computation has b…
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…
VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
Arash Ardakani, François Leduc-Primeau, Naoya Onizawa +2
The hardware implementation of deep neural networks (DNNs) has recently received tremendous attention: many applications in fact require high-speed operations that suit a hardware…
Associative Memories Based on Multiple-Valued Sparse Clustered Networks
Hooman Jarollahi, Naoya Onizawa, Takahiro Hanyu +1
Associative memories are structures that store data patterns and retrieve them given partial inputs. Sparse Clustered Networks (SCNs) are recently-introduced binary-weighted associ…