Application of neural networks incorporated with real-valued genetic algorithms in knowledge acquisition

Energy-Efficient Multiobjective Thermal Control for Liquid-Cooled 3-D Stacked Architectures

“…Although the same approach can be adapted to use different cyber-physical controllers, we opt to use rule-based fuzzy controller instead of other linear multi-input multioutput (MIMO) controllers [13] as with this technique, we are able to achieve effective control with a straightforward, low-complexity, and flexible implementation. Various lowcomplexity techniques can be used for deriving the fuzzy rules, such as offline analytical analysis and online learning mechanisms [37], and fuzzy control can be implemented at the software-level with low overhead, as we show in Section VII. Moreover, fuzzy control operates efficiently at run-time with inputs that have a degree of uncertainty in describing the system state [26], which is the case in 3-D MPSoCs where various inputs can be affected by a number of conditions (e.g., ambient temperature changes, unexpected workloads, temperature sensors inaccuracy, and stack degradation).…”

Temperature-Aware Design and Management for 3D Multi-Core Architectures

“…analytical analysis and on-line learning mechanisms [117], and fuzzy control can be implemented at the software-level with low overhead. Moreover, fuzzy control operates efficiently at run-time with inputs that have a degree of uncertainty in describing the system state [118], which is the case in 3D MPSoCs where various inputs can be affected by a number of conditions (e.g., ambient temperature changes, unexpected workloads, temperature sensors inaccuracy, stack degradation, etc.).…”

Fuzzy control for enforcing energy efficiency in high-performance 3D systems

“…This acquisition can be achieved by utilizing expert knowledge or by other techniques such as genetic algorithms [24]. In our derivation, we rely on offline thermal response analysis to observe how each processing element is affected by each control variable.…”