Finite State Machine (FSM) synthesis traditionally starts with state minimization and state encoding, which provide codes of minimal length to the FSM with minimal number of states. Recently, there have been studies on encoding with nonminimal length and synthesis on non-minimized FSM

Due to reduction in device feature size and supply voltages the probability of soft-errors in Finite State Machines ’ (FSMs) states has increased dramatically, and the protection against both Single Event Upset (SEU) and Multiple Bit Upsets (MBUs) soft-errors demand for design of fault tolerant

Abstract — It is not easy to measure the functional size of

, is mandatory. This is much more complex than traversing just one of the component machines. This paper proposes an equivalence-preserving function that transforms the product machine in the General Product Machine (GPM). Using the GPM in symbolic state space traversal reduces the size of the BDDs and makes

Whole-genome copy number analysis platforms, such as array comparative genomic hybridization (aCGH) and single

Abstract- This paper presents a fast GPU implementation of a genetic algorithm for synthesizing bimodal predictor FSMs of a given size. Bimodal predictors, i.e., predictors that make binary yes/no predictions, are ubiquitous in mi-croprocessors. Many of these predictors are based on fi

. It exteids the ex-pression capabilities by concurrency, hierarchy, and state variable while it maintains formal property. Because of formal-ity and the structured nature of fFSM model, we can apply a static analysis method to find ambiguous behavior and synthe-size softwardhardware automatically, which

Abstract: Since modern programmable devices contain embedded memory blocks, there exists a possibility to implement Finite State Machines (FSM) using such blocks. The size of the memory available in programmable devices is limited, though. The paper presents a general method for the synthesis

We propose Finite State Machine (FSM) re-engineering, a performance enhancement framework for FSM synthesis and optimization. It starts with the traditional FSM synthesis pro-cedure, then proceeds to re-construct a functionally equivalent but topologically different FSM based on the optimization

machines (FSMs) for policy representation and expoits these FSMs to provide three key contributions: (i) Not all agents within an agent network need the same expressivity of policy representation; FANS introduces novel heuristics to automatically vary the FSM size in different agents for scaleup;