Institution profile

Tohoku University

Academic institutionasia · jp
Official website
Research library412linked papers
Opportunities0open roles
Selected work

Representative Papers

Analog-to-Stochastic Converter Using Magnetic Tunnel Junction Devices for Vision Chips

Sep 01, 2016IEEE transactions on nanotechnology

This work addresses the high power and area overhead of conventional analog-to-stochastic signal conversion, which typically requires two separate stages—analog-to-digital followed by digital-to-stochastic—rendering it unsuitable for energy-efficient vision chips. To overcome this limitation, the study proposes a novel single-step direct conversion approach leveraging the intrinsic probabilistic switching behavior of magnetic tunnel junctions (MTJs). This method significantly reduces hardware complexity and improves energy efficiency. Furthermore, to mitigate the impact of MTJ resistance variability, a compensation mechanism is introduced to enhance conversion accuracy and robustness. Mixed-mode NS-SPICE simulations based on 90 nm CMOS and 100 nm MTJ technologies demonstrate the proposed circuit’s advantages in terms of reduced area, lower power consumption, and improved tolerance to device variations.

29 citationsRead paper

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

Jan 16, 2024Scientific Reports

This work addresses the challenge of energy stagnation and poor convergence in probabilistic-bit simulated annealing (pSA) when solving large-scale combinatorial optimization problems, which arises from oscillatory dynamics inherent to p-bits. The study is the first to identify these oscillations as stemming from the system’s feedback mechanism and proposes an innovative strategy that suppresses them by selectively deactivating a subset of p-bits. Building on this insight, two novel algorithms—time-averaged pSA (TApSA) and stagnation-aware pSA (SpSA)—are developed. Evaluated through p-bit hardware modeling, Ising formulation, and Python-based simulations on 16 Max-Cut benchmark instances with 800 to 5,000 nodes, the proposed methods achieve an average improvement of 0.8%–98.4% in normalized cut values over conventional pSA, demonstrating substantially enhanced convergence performance.

10 citationsRead paper

GPU-accelerated simulated annealing based on p-bits with real-world device-variability modeling

Feb 19, 2025Scientific Reports

This work addresses the impact of device variability—specifically in timing, coupling strength, and bias offset—on the performance of simulated annealing in physical p-bit hardware by introducing the first open-source, GPU-accelerated framework capable of modeling such non-idealities. Leveraging CUDA for efficient large-scale simulation, the study reveals, for the first time, that timing variability can enhance rather than degrade algorithmic performance. Validated on the MAX-CUT benchmark, the framework achieves up to two orders of magnitude speedup over CPU-based implementations across problem sizes ranging from 800 to 20,000 nodes, demonstrating both the efficacy of incorporating realistic device variations and the scalability of the proposed platform.

5 citationsRead paper

Assembling Solar Panels by Dual Robot Arms Towards Full Autonomous Lunar Base Construction

Jan 21, 2025IEEE/SICE International Symposium on System Integration

This work proposes a fully autonomous, modular solar panel assembly system tailored for lunar environments to support future lunar base construction. Addressing challenges such as low gravity and the absence of GPS, the system integrates a dual robotic arm with cooperative control, a specialized grasping connector, and a hybrid active-passive docking mechanism. An end-to-end autonomous assembly pipeline is developed, incorporating real-time visual localization, pose estimation, and motion planning. Experiments conducted in a simulated lunar environment demonstrate successful automatic identification, alignment, and reliable connection of solar panel modules from arbitrary initial poses, confirming the system’s high robustness and engineering feasibility for complex space missions.

5 citationsRead paper

Local-Polynomial Estimation for Multivariate Regression Discontinuity Designs

Feb 14, 2024

In multivariate regression discontinuity designs (RDD), conventional dimensionality reduction of multidimensional running variables to Euclidean distance leads to suboptimal bandwidth selection, inefficient estimation, and inability to detect heterogeneous treatment effects on the cutoff boundary. This paper proposes a direct local linear estimation framework for multivariate running variables. It establishes, for the first time, the asymptotic normality theory for multivariate local polynomial estimators, enabling boundary-adaptive bandwidth selection and precise identification of heterogeneous treatment effects. The method integrates multivariate local linear regression, asymptotic statistical inference, and numerical simulation, and is applied to evaluate Colombia’s scholarship policy. Results demonstrate substantially improved estimation efficiency and uncover rich heterogeneity masked by traditional univariate approaches—thereby overcoming fundamental limitations of the dimensionality-reduction paradigm.

2 citations1 influentialRead paper
Recent publications

Latest Papers