A Multi-Objective Perspective for Operator Scheduling Using Finegrained DVS Architectures

Abstract

The stringent power budget of fine grained power managed digital integrated circuits have driven chip designers to optimize power at the cost of area and delay, which were the traditional cost criteria for circuit optimization. The emerging scenario motivates us to revisit the classical operator scheduling problem under the availability of DVFS enabled functional units that can trade-off cycles with power. We study the design space defined due to this trade-off and present a branch-and-bound(B/B) algorithm to explore this state space and report the pareto-optimal front with respect to area and power. The scheduling also aims at maximum resource sharing and is able to attain sufficient area and power gains for complex benchmarks when timing constraints are relaxed by sufficient amount. Experimental results show that the algorithm that operates without any user constraint(area/power) is able to solve the problem for most available benchmarks, and the use of power budget or area budget constraints leads to significant performance gain.

Development of Multiobjective High-Level Synthesis for FPGAs

“…Figure 5 is an example of the graph, the paper [26] is the gray node, the multiobjective method is a branch and bound, Pareto dominance is the cost assignment strategy, DFG and CDFG are the compiler techniques, while branch and X is the MOA, area and power are the objective functions, and finally, experiments were carried out on the CDFG toolset benchmark. In this case, the gray nodes [27,28] are the papers that have at least one author in common with respect to the blue node.…”

“…Over a CDFG, a multiobjective optimization is carried out with a Pareto dominance technique considering area and power as metrics (see Figure 5). One year later, in [28], published by the same authors, a biobjective proposal with similar characteristics was presented, taking into account FUs that support dynamic voltage and frequency. e publications of the branch and X method are scarce, due to their ineffectiveness dealing with multiobjective problems and their high probability of being stuck in a local optimum.…”