Recently a number of heuristic based system-level synthesis algorithms have been proposed. Though these algorithms quickly generate good solutions, how close these solutions are to optimal is a question that is difficult to answer. While current exact techniques produce optimal results, they fail to produce them in reasonable time. This paper presents a synthesis algorithm that produces solutions of guaranteed quality (optimal in most cases or within a known bound) with practical synthesis times (few seconds to minutes). It takes a unified look (the lack of which is one of the main sources of sub-optimality in the heuristic techniques) at different aspects of system synthesis such as pipelining, selection, allocation, scheduling and FPGA reconfiguration. Our technique can handle both time constrained as well as resource constrained synthesis problems. We present results of our algorithm implemented as part of the Match project [1] at Northwestern University.
Abstract. This paper deals with the problem of statically inferring the shape of an array in languages such as MATLAB. Inferring an array's shape is desirable because it empowers better compilation and interpretation; specifically, knowing an array's shape could permit reductions in the number of run-time array conformability checks, enable memory preallocation optimizations, and facilitate the in-lining of "scalarized" code. This paper describes how the shape of a MATLAB expression can be determined statically, based on a methodology of systematic matrix formulations. The approach capitalizes on the algebraic properties that underlie MATLAB's shape semantics and exactly captures the shape that the MATLAB expression assumes at run time. Some of the highlights of the approach are its applicability to a large class of MATLAB functions and its uniformity. Our methods are compared with the previous shadow variable scheme, and we show how the algebraic view allows inferences not deduced by the traditional approach.
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