This paper proposes a novel method for extracting Finite State Machines (FSMs) from flattened gate-level netlist. The proposed method which employs a potential state register elimination technique and a two-level FSM separation strategy is highly applicable to control-intensive circuits. The potential state register elimination technique is based on control signal identification whereas the two-level FSM separation strategy is based on enable tree identification and the strongly connected components algorithm. To demonstrate the efficacy and to illustrate the unique features of the proposed FSM extraction method, the Synopsys DesignWare DW8051 microcontroller is used as the benchmark circuit for comparison and simulations. Results show that the proposed method reduces the complexity of the extracted FSMs in terms of number of state registers in an FSM by more than 90% as compared to the reported technique.
This paper presents a novel blood glucose regulation for type I (insulin-dependent) diabetes mellitus patients using biologically inspired TSK0-FCMAC, a fuzzy cerebellar model articulation controller (CMAC) based on the zero-ordered Takagi-Sugeno-Kang (TSK) fuzzy inference scheme. TSK0 -FCMAC is capable of performing localized online training with an effective fuzzy inference scheme that could respond swiftly to changing environment such as human's endocrine system. Without prior knowledge of disturbance (e.g., food intake), the proposed fuzzy CMAC is able to capture the glucose-insulin dynamics of individuals under different dietary profiles. Preliminary simulations show that the blood glucose level is kept within the state of euglycemia. The design of the proposed system follows closely to what is available in real life and is suitable for animal and clinical pilot testing in the near future.
Artificial neural networks (ANNs) are systems that are deliberately constructed to make use of some organizational principles resembling those in the human brain. ANNs have a large number of highly interconnected processing elements (perceptrons) that usually operate in parallel and are configured in regular architectures. The collective behavior of an ANN, like a human brain, demonstrates the ability to learn, recall, and generalize from training patterns or data. They are good at tasks such as classification, function approximation, optimization, and data clustering [1]. The cerebellar model articulation controller (CMAC), a perceptron-like associative memory equipped with overlapping receptive field proposed by Albus [2], belongs to a special category of ANNs. It was first applied in the domain of control problems. During the past decades, its ability to capture nonlinear function has been demonstrated through many applications in control, function approximation and pattern recognition. On the other hand, the development of fuzzy systems suffered from decades of controversy ever since the first fuzzy set theory proposed by Prof Lotfi A. Zadeh in 1965 [3]. It aims to alleviate difficulties in developing complex systems without mathematically analyzing the dynamics of the problem. It provides an intuitive channel between different facets of a problem; from quantitative aspect to qualitative aspect and vice versa. However, the development of fuzzy systems in the early days required the manual tuning of the system parameters based on observation of the system performance and this shortcoming has become a major criticism on the application of fuzzy set theory.
A generic and highly versatile method for extracting functional modules from a flattened gate-level netlist is proposed. The proposed method requires no prior knowledge about the netlist under analysis and is applicable to circuits targeting diverse applications. It is fully automated and employs a highly compact module library containing only single generic model for each common function type with arbitrary data width. Experiment results depict the efficacy of the proposed method and its embodied techniques.
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