Abstract: Real-world software–hardware co-design for AI accelerators must meet strict constraints on accuracy and PPA, making design space exploration both costly and inefficient. In this work, we ...

Abstract: Bayesian Optimization (BO) has shown promise in tuning processor design parameters. However, standard BO does not support constraints involving categorical parameters such as types of branch ...

Develop optimal solutions to a scheduling problem by modelling it as a Constraint Satisfaction Problem (CSP), a method used widely in the field of Artificial Intelligence. I've open-sourced Delegator ...

Kentaro Matsuura (2023). Bayesian Statistical Modeling with Stan, R, and Python. Singapore: Springer. URL: https://link.springer.com/book/10.1007/978-981-19-4755-1 ...

Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russian Federation Academic University, Russian Academy of Sciences, St. Petersburg 194021, Russian Federation ...

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...