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. By addressing both steady-state and transient-state scenarios, SAfEPaTh effectively captures the dynamic effects of pipeline bubbles in interlayer pipelines, utilizing real CNN workloads for comprehensive evaluation. Unlike traditional methods, it eliminates the need for circuit-level simulations or on-chip measurements. Our methodology leverages TANIA, a cutting-edge hybrid digital-analog tile-based accelerator featuring analog-in-memory computing cores alongside digital cores. 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. This efficiency and accuracy make SAfEPaTh an invaluable tool for designers, enabling them to optimize performance while adhering to stringent power and thermal constraints. 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.

翻译：设计高能效、高性能且可靠的卷积神经网络（CNN）加速器面临严峻挑战，这主要源于复杂的功耗与热管理问题。本文提出SAfEPaTh，一种新颖的系统级方法，用于精确估计基于瓦片架构的CNN加速器的功耗与温度。通过同时处理稳态与瞬态场景，SAfEPaTh有效捕捉了层间流水线中流水线气泡的动态效应，并利用真实的CNN工作负载进行全面评估。与传统方法不同，它无需电路级仿真或片上测量。我们的方法基于TANIA——一种先进的混合数字-模拟瓦片式加速器，其集成了模拟存内计算核心与数字核心。通过使用ResNet18模型进行严格仿真实验，我们证明了SAfEPaTh能在500秒内精确估计功耗与温度，其过程涵盖CNN模型加速器映射探索以及详细的功耗与热估计。这种高效性与精确性使SAfEPaTh成为设计人员的宝贵工具，使其能够在满足严格功耗与热约束的同时优化性能。此外，SAfEPaTh的适应性使其可广泛应用于各类CNN模型与加速器架构，凸显了其在该领域的广泛适用性。本研究对推动高能效、可靠CNN加速器设计的发展做出了重要贡献，解决了动态功耗与热管理中的关键挑战。