Constrained Hardware Dimensioning for AI Algorithms

Given the diffusion of Artificial Intelligence (AI) in numerous domains, experts and practitioners are faced with the challenge of finding the optimal hardware (HW) resources and configuration (hardware dimensioning) under different con- straints and objectives (e.g., budget, time, solution quality). To tackle this challenge, we propose an automated tool for HArdware Dimensioning of (AI) Algorithms (HADA), an approach relying on the integration of Machine Learning (ML) models together into an optimization problem, where experts domain knowledge can be injected as well. The ML models encapsulate the data-driven knowledge about the relationships between HW requirements and AI algorithm performances. We show how HADA can be employed to find the best HW configuration that respects user-defined constraints in three different domains