SAfEPaTh: A System-Level Approach for Efficient Power and Thermal Estimation of Convolutional Neural Network Accelerator

--The design of energy-efficient, high-performance, and reliable Convolutional Neural Network (CNN) accelerators involves significant challenges due to complex power and thermal management issues. This paper introduces SAfEPaTh, a novel system-level approach for accurately estimating power and temperature in tile-based CNN accelerators. Unlike traditional methods, it eliminates the need for circuit-level simulations or on-chip measurements. Through rigorous simulation results using the ResNet18 model, we demonstrate SAfEPaTh's capability to accurately estimate power and temperature within 500 seconds, encompassing CNN model accelerator mapping exploration and detailed power and thermal estimations. Furthermore, SAfEPaTh's adaptability extends its utility across various CNN models and accelerator architectures, underscoring its broad applicability in the field. This study contributes significantly to the advancement of energy-efficient and reliable CNN accelerator designs, addressing critical challenges in dynamic power and thermal management. I NTRODUCTION Convolutional Neural Network (CNN) accelerators have become essential for many modern applications, such as autonomous driving, image and speech recognition, and natural language processing, all of which demand low power consumption, high performance, and high reliability [1]. However, as semiconductor scaling approaches its physical limits, managing dynamic power and thermal issues in accelerators executing CNN models poses increasing challenges.