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Model-Based Design for Embedded Systems- P64: This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. | Higher structural hierarchical level n 1 Parameter extraction method Performance criteria Aggregation Specification values Error function calculation 1-Step optimization Design variables -physical parameters Model parameter values Constraint distribution method Distribution Lower structural hierarchical level n- 1 FIGURE 19.4 Single-level AMS MT synthesis loop showing context of AMS MT IP facet use. Components of soft-IP_ library Performance criteria S Design variables V Physical parameters P Evaluation and parameter extraction methods Design variables to physical parameter conversion method Synthesis method Constraint distribution method Model-Based Design for Embedded System Platform for Model-Based Design of Integrated Multi-Technology Systems 617 wj i e 0 n - 1 and normalized squared differences subject to specification type constraint cost condition etc. i n 1 x 2 E S-i Sri wi i 0 Si This comparison between specified and real performance criteria values the error function in Figure 19.4 drives m the synthesis method which describes the route to determine design variable values. It is possible to achieve this in two main ways Through a direct procedure definition if the design problem has sufficient constraints to enable the definition of an explicit solution. Through an iterative optimization algorithm. If the optimization process cannot as is usually the case be described directly in the language used to describe the IP block then a communication model must be set up between the optimizer and the evaluation method. A direct communication model gives complete control to the optimization process while an inverse communication model uses an external process to control data flow and synchronization between optimization and evaluation. The latter model is less efficient but makes it easier to retain tight control over the synthesis process MAS1991 . The synthesis method generates a new set V of combinations of design variables as exploratory points in the design .
