There is a high demand to reduce the energy consumption of microprocessors. Among resources in a processor, the issue queue is one of the largest energy consumers, with much of the energy being consumed by the wakeup logic. The wakeup logic comprises the content-addressable memory, where tag comparisons are performed for all entries. Such global tag comparisons consume an appreciable amount of energy. This paper proposes a scheme called segmenting wakeup logic (SegWU), where the wakeup logic is segmented logically. An instruction is dispatched to an available entry in the segment selected based on the partial bits of the source tag values. At the wakeup time, a broadcast destination tag is compared only in the segment that corresponds to the partial bits of the destination tag value. Tag comparisons are not performed in the other segments. This localization of tag comparisons reduces the energy consumption. Our evaluation results for SPEC2017 benchmark programs show that SegWU reduces the number of tag comparisons by 90.3%. This lowers the IQ energy consumption, and the net reduction excluding the secondary effect (activity lowering of the front-end) and overhead is 3.0% in the base core energy consumption with no performance degradation. The overall energy reduction including the secondary effect is 6.9%.

This paper presents a sum of squares (SOS) approach for modeling and control of nonlinear dynamical systems using polynomial fuzzy systems. The proposed SOS-based framework provides a number of innovations and improvements over the existing linear matrix inequality (LMI)-based approaches to Takagi-Sugeno (T-S) fuzzy modeling and control. First, we propose a polynomial fuzzy modeling and control framework that is more general and effective than the well-known T--S fuzzy modeling and control. Secondly, we obtain stability and stabilizability conditions of the polynomial fuzzy systems based on polynomial Lyapunov functions that contain quadratic Lyapunov functions as a special case. Hence, the stability and stabilizability conditions presented in this paper are more general and relaxed than those of the existing LMI-based approaches to T-S fuzzy modeling and control. Moreover, the derived stability and stabilizability conditions are represented in terms of SOS and can be numerically (partially symbolically) solved via the recently developed SOSTOOLS. To illustrate the validity and applicability of the proposed approach, a number of analysis and design examples are provided. The first example shows that the SOS approach renders more relaxed stability results than those of both the LMI-based approaches and a polynomial system approach. The second example presents an extensive application of the SOS approach in comparison with a piecewise Lyapunov function approach. The last example is a design exercise that demonstrates the viability of the SOS-based approach to synthesizing a stabilizing controller.