Peephole Optimization of Asynchronous Networks Through Process Composition and Burst mode Machine Generation

In this paper we discuss the problem of improving the e ciency of macromodule networks generated through asynchronous high level synthesis We compose the behaviors of the modules in the sub network being optimized using Dill s trace theoretic operators to get a single behavioral description for the whole sub network From the composite trace structures so obtained we obtain interface state graphs ISG as described by Sutherland Sproull and Molnar encode the ISGs to obtain encoded ISGs EISGs and then apply a procedure we have developed called Burst mode machine reduction BM reduction to obtain burst mode machines from EISGs We then synthesize burst mode machine circuits currently using the tool of Ken Yun Stanford We can report signi cant area and time improvements on a number of examples as a result of our optimization method This work was supported in part by NSF Award MIP Peephole Optimization of Asynchronous Networks through Process Composition and Burst mode Machine Generation GANESH GOPALAKRISHNAN ganesh cs utah edu PRABHAKAR KUDVA pkudva cs utah edu Department of Computer Science University of Utah Salt Lake City Utah USA