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Raji, Mohsen, Ghavami, Behnam, Keshavarzi, Saeed, Mahmoudi, Reza. (1403). GENSO: A Genetic Algorithm-based Optimization Framework for Lifetime Reliability Enhancement in Sequential Circuit Design. هنرهای کاربردی, 4(4), 41-50. doi: 10.22075/mseee.2025.37658.1212
Mohsen Raji; Behnam Ghavami; Saeed Keshavarzi; Reza Mahmoudi. "GENSO: A Genetic Algorithm-based Optimization Framework for Lifetime Reliability Enhancement in Sequential Circuit Design". هنرهای کاربردی, 4, 4, 1403, 41-50. doi: 10.22075/mseee.2025.37658.1212
Raji, Mohsen, Ghavami, Behnam, Keshavarzi, Saeed, Mahmoudi, Reza. (1403). 'GENSO: A Genetic Algorithm-based Optimization Framework for Lifetime Reliability Enhancement in Sequential Circuit Design', هنرهای کاربردی, 4(4), pp. 41-50. doi: 10.22075/mseee.2025.37658.1212
Raji, Mohsen, Ghavami, Behnam, Keshavarzi, Saeed, Mahmoudi, Reza. GENSO: A Genetic Algorithm-based Optimization Framework for Lifetime Reliability Enhancement in Sequential Circuit Design. هنرهای کاربردی, 1403; 4(4): 41-50. doi: 10.22075/mseee.2025.37658.1212

GENSO: A Genetic Algorithm-based Optimization Framework for Lifetime Reliability Enhancement in Sequential Circuit Design

Journal of Modeling and Simulation in Electrical and Electronics Engineering
دوره 4، شماره 4 - شماره پیاپی 18، اسفند 2024، صفحه 41-50    اصل مقاله (829.37 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/mseee.2025.37658.1212
نویسندگان
Mohsen Raji* 1؛ Behnam Ghavami2؛ Saeed Keshavarzi1؛ Reza Mahmoudi1
1School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
2School of Engineering Science at Simon Fraser University, Canada.
تاریخ دریافت: 15 اردیبهشت 1404،  تاریخ بازنگری: 01 تیر 1404،  تاریخ پذیرش: 26 شهریور 1404 
چکیده
Chnology scaling becomes increasingly aggressive, lifetime reliability has emerged as a critical challenge for modern digital circuits, exacerbated by manufacturing process variations and aging effects. This paper introduces GenSO, a Genetic algorithm-based multi-objective Sequential circuit Optimization framework designed to enhance the lifetime reliability of sequential circuits modeled as Finite State Machines (FSMs), while simultaneously addressing initial delay and power consumption. The framework leverages a cross-layer approach, utilizing a gate-level delay degradation model that accounts for process variations and aging to estimate circuit lifetime reliability. A novel metric, termed Guardband-Aware Reliability (GAR), is proposed to provide a fair assessment of FSM lifetime reliability in relation to the guardband and timing yield specified by the designer. A multi-objective genetic algorithm is then employed to optimize delay, power consumption, and lifetime reliability in FSM-based sequential circuits. Experimental results demonstrate that GenSO successfully identifies non-dominated solutions for sequential circuit designs, achieving simultaneous optimization of initial delay, power consumption, and lifetime reliability. With a 15% delay overhead for a 6-year lifetime and a 10% variation ratio, GenSO improves circuit reliability by an average of 64.34%, significantly outperforming state-of-the-art reliability optimization frameworks, which typically achieve less than 30% improvement in lifetime reliability.
کلیدواژه‌ها
Finite State Machines؛ Lifetime Reliability؛ Multi-objective Optimization؛ Process Variations؛ BTI
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