3,676 research outputs found

    Finite State Machines With Input Multiplexing: A Performance Study

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    Finite state machines with input multiplexing (FSMIMs) have been proposed in previous works as a technique for efficient mapping FSMs into ROM memory. In this paper, we propose a new architecture for implementing FSMIMs, called FSMIM with state-based input selection, whose goal is to achieve a further reduction in memory usage. This paper also describes in detail the algorithms for generating FSMIMs used by the tool FSMIM-Gen, which has been developed and made available on the Internet for free public use. A comparative study in terms of speed and area between FSMIM approaches and other field programmable gate array-based techniques is presented. The results show that the FSMIM approaches obtain huge reductions in the look-up table (LUT) usage by using a small number of embedded memory blocks. In addition, speed improvements over conventional LUT-based implementations have been obtained in many cases

    An FSM Re-Engineering Approach to Sequential Circuit Synthesis by State Splitting

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    We propose Finite State Machine (FSM) re-engineering, a performance enhancement framework for FSM synthesis and optimization. It starts with the traditional FSM synthesis procedure, then proceeds to re-construct a functionally equivalent but topologically different FSM based on the optimization objective, and concludes with another round of FSM synthesis on the re-constructed FSM. This approach explores a larger solution space that consists of a set of FSMs functionally equivalent to the original one, making it possible to obtain better solutions than in the original FSM. Guided by the result from the #2;rst round of synthesis, the solution space exploration process can be rapid and cost-ef#2;cient. We apply this framework to FSM state encoding for power minimization and area minimization. The FSM is #2;rst minimized and encoded using existing state encoding algorithms. Then we develop both a heuristic algorithm and a genetic algorithm to re-construct the FSM. Finally, the FSM is reencoded by the same encoding algorithms. To demonstrate the effectiveness of this framework, we conduct experiments on MCNC91 sequential circuit benchmarks. The circuits are read in and synthesized in SIS environment. After FSM re-engineering are performed, we measure the power, area and delay in the newly synthesized circuits. In the powerdriven synthesis, we observe an average 5.5% of total power reduction with 1.3% area increase and 1.3% delay increase. This results are in general better than other low power state encoding techniques on comparable cases. In the area-driven synthesis, we observe an average 2.7% area reduction, 1.8% delay reduction, and 0.4% power increase. Finally, we use integer linear programming to obtain the optimal low power state encoding for benchmarks of small size. We #2;nd that the optimal solutions in the re- engineered FSMs are 1% to 8% better than the optimal solutions in the original FSMs in terms of power minimization

    An integrated search-based approach for automatic testing from extended finite state machine (EFSM) models

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    This is the post-print version of the Article - Copyright @ 2011 ElsevierThe extended finite state machine (EFSM) is a modelling approach that has been used to represent a wide range of systems. When testing from an EFSM, it is normal to use a test criterion such as transition coverage. Such test criteria are often expressed in terms of transition paths (TPs) through an EFSM. Despite the popularity of EFSMs, testing from an EFSM is difficult for two main reasons: path feasibility and path input sequence generation. The path feasibility problem concerns generating paths that are feasible whereas the path input sequence generation problem is to find an input sequence that can traverse a feasible path. While search-based approaches have been used in test automation, there has been relatively little work that uses them when testing from an EFSM. In this paper, we propose an integrated search-based approach to automate testing from an EFSM. The approach has two phases, the aim of the first phase being to produce a feasible TP (FTP) while the second phase searches for an input sequence to trigger this TP. The first phase uses a Genetic Algorithm whose fitness function is a TP feasibility metric based on dataflow dependence. The second phase uses a Genetic Algorithm whose fitness function is based on a combination of a branch distance function and approach level. Experimental results using five EFSMs found the first phase to be effective in generating FTPs with a success rate of approximately 96.6%. Furthermore, the proposed input sequence generator could trigger all the generated feasible TPs (success rate = 100%). The results derived from the experiment demonstrate that the proposed approach is effective in automating testing from an EFSM

    Dynamic load balancing for the distributed mining of molecular structures

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    In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids

    Enhancing Power Efficient Design Techniques in Deep Submicron Era

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    Excessive power dissipation has been one of the major bottlenecks for design and manufacture in the past couple of decades. Power efficient design has become more and more challenging when technology scales down to the deep submicron era that features the dominance of leakage, the manufacture variation, the on-chip temperature variation and higher reliability requirements, among others. Most of the computer aided design (CAD) tools and algorithms currently used in industry were developed in the pre deep submicron era and did not consider the new features explicitly and adequately. Recent research advances in deep submicron design, such as the mechanisms of leakage, the source and characterization of manufacture variation, the cause and models of on-chip temperature variation, provide us the opportunity to incorporate these important issues in power efficient design. We explore this opportunity in this dissertation by demonstrating that significant power reduction can be achieved with only minor modification to the existing CAD tools and algorithms. First, we consider peak current, which has become critical for circuit's reliability in deep submicron design. Traditional low power design techniques focus on the reduction of average power. We propose to reduce peak current while keeping the overhead on average power as small as possible. Second, dual Vt technique and gate sizing have been used simultaneously for leakage savings. However, this approach becomes less effective in deep submicron design. We propose to use the newly developed process-induced mechanical stress to enhance its performance. Finally, in deep submicron design, the impact of on-chip temperature variation on leakage and performance becomes more and more significant. We propose a temperature-aware dual Vt approach to alleviate hot spots and achieve further leakage reduction. We also consider this leakage-temperature dependency in the dynamic voltage scaling approach and discover that a commonly accepted result is incorrect for the current technology. We conduct extensive experiments with popular design benchmarks, using the latest industry CAD tools and design libraries. The results show that our proposed enhancements are promising in power saving and are practical to solve the low power design challenges in deep submicron era
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