Utilizing Predictors for Efficient Thermal Management in Multiprocessor SoCs
Abstract
Conventional thermal management techniques are reactive, as they take action after temperature reaches a threshold. Such approaches do not always minimize and balance the temperature, and they control temperature at a noticeable performance cost. This paper investigates how to use predictors for forecasting temperature and workload dynamics, and proposes proactive thermal management techniques for multiprocessor system-on-chips. The predictors we study include autoregressive moving average modeling and lookup tables. We evaluate several reactive and predictive techniques on an UltraSPARC T1 processor and an architecture-level simulator. Proactive methods achieve significantly better thermal profiles and performance in comparison to reactive policies.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2009
- Accession Number
- ADA542649
Entities
People
- Ayse K. Coskun
- Kenny C. Gross
- Tajana S. Rosing
Organizations
- University of California, San Diego