Control flow transformation

These data- and control-flow transformations are based on an extended poly-hedral dependence graph (PDG) model [23] (see also chapter 7), in terms of which the actual optimization problem is defined and the transformations are performed. In this section, a short overview is given of the main concepts of the PDG. Detailed information can be found elsewhere [23, 22]. 

M. van Swaaij, F. Pranssen, F. Catthoor, and H. De Man. Automating high-level control flow transformations for DSP memory management. In Proc. IEEE workshop on VLSI signal processing, Napa Valley CA, Oct 1992. 

The transformations used by SUGAR fall into two main categories control flow transformations and data flow transformations. Control flow transformations (e.g. in-line expansion of procedure calls) alter the control flow of the behavior while data flow transformations (e.g. constant folding) preserve the control flow but alter the data flow. The transformations in the Workbench s general synthesis path operate on a VT, while the transformations in SUGAR operate on TCOL trees. A small subset of the transformations defined by Snow have been... 

On the other hand, control flow transformation approaches[14, 13], which move operations among c-steps, are considering conditional branches and loops as well as data dependencies. They can produce different controls for each path in conditional branches, but they cannot exploit maximal potential parallel operations, because it is very difficult for them to violate the control structure of the behavior description. 

One is a control flow transformation approach, and the other is an approach based on data flow dependencies and control structure analysis. 

The application of microchannel technology is a natural fit for the production of synthetic fuels via the FT process. The primary limitations of conventional FT technology include the removal of process heat that can produce hot spots and severely shorten catalyst life, and effective management of two-phase flow as synthesis gas transforms into hquid hydrocarbons. Both these issues can be addressed with microchaimel technology, which greatly improves heat transfer and precisely controls flow through thousands of parallel chaimels. 

Mechanistic Multiphase Model for Reactions and Transport of Phosphorus Applied to Soils. Mansell et al. (1977a) presented a mechanistic model for describing transformations and transport of applied phosphorus during water flow through soils. Phosphorus transformations were governed by reaction kinetics, whereas the convective-dispersive theory for mass transport was used to describe P transport in soil. Six of the kinetic reactions—adsorption, desorption, mobilization, immobilization, precipitation, and dissolution—were considered to control phosphorus transformations between solution, adsorbed, immobilized (chemisorbed), and precipitated phases. This mechanistic multistep model is shown in Fig. 9.2. 

Hybrid Error-detection Technique using Assertions (HETA) is the third and final hybrid technique presented in this book. It was initially based in the CEDA software-based technique and its abiUty to efiSciently detect control flow errors between different BBs, and PODER and its ability to detect control flow errors inside the same BB. HETA is aimed at both FPGAs and ASICs, since it implements a non-intrasive hardware module combined with transformation rules on the program code. 

The example design of a motion estimation application using the design script resulted in an architecture that can be compared with even the best manual designs. The careful selection of the transformation matrix produced an architecture with optimal I/O and control flow. Only control logic and I/O at the borders is needed, and the interface buffer sizes needed are very small. This shows that the proposed methodology and tools can indeed be used for complex real-life examples, with optimal results. 

Given this placement, an optimized control flow can now be expressed by applying a single affine transformation on the complete common node space in such a way that the new base vectors (iterators) indicate the sequence of loop ordering [31]. 

As a first step, the HardwareC compiler performs a profound data/control-flow analysis. A DFG optimizer is directly coupled to the compiler. After optimization, the data/control-fiow graph will be stored as a combined single flow graph which can be considered as a data base. All behavioral synthesis transformations require input from this data base and will produce output in the graph format defined in chapter 2. 

Physical synthesis is a multi-phase optimization process performed during IC design to achieve timing closure, though area, routability, power and yield must be optimized as well. Individual steps in physical synthesis, called transformations are invoked by dynamic controller functions in complex sequences called design flows (EDA flows). Transformations rely on abstract delay models to analyze timing requirements and guide optimization, as illustrated in Sect. 2.3. Finally, we describe recent evolution of requirements for physical synthesis and discuss current trends. 

Secondly, the checks run by the verifying compilers are usually not based on abstract interpretation. They are mostly realized as abstract syntax tree transformations much in the line with the supporting routines of the compilation process (data and control flow graph analysis, dead code elimination, register allocation, etc.) and the evaluation function is basically the matching of antipatterns of common programming bugs. 

The source code is restructured to [ux>vide an easily understood representation by improving control flow, removing dead code and, importantly, introducing a good procedural structure. Transformations are used to accomplish this restructuring. 

In essence the TCMC method consists in the main transformer magnetic flow redistribution between magnetic circuits of the middle one, which composed by uncontrollable lateral yoke and a number of rods leaned on controllable middle yoke and lateral one, composed by the same uncontrollable yoke and rods leaned on lateral yoke. 

Fig. 4.1 Block diagram of a closed-loop control system. R s) = Laplace transform of reference input r(t) C(s) = Laplace transform of controlled output c(t) B s) = Primary feedback signal, of value H(s)C(s) E s) = Actuating or error signal, of value R s) - B s), G s) = Product of all transfer functions along the forward path H s) = Product of all transfer functions along the feedback path G s)H s) = Open-loop transfer function = summing point symbol, used to denote algebraic summation = Signal take-off point Direction of information flow.

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