Control data flow graph

Intermediate form Control data flow graph and Structured control graph... 

The next step is to take the sum of the probabilities of each type of operation for each c-step of the control/data flow graph. The resulting distribution graphs (DGs) indicate the concurrency of similar operations. For each DG the distribution in c-step / is given by ... 

Contrary to many control data-flow graph formats, the if-then else and case control blocks of the HAL system do not have any implicit boundaries [4]. The control blocks only serve to indicate which operations are part of which branch. Fork and join blocks can be nested to any depth. An example with multiple nested foik/join blocks is presented elsewhere [3]. 

Correctness is defined with respect of the final results of evaluating a data flow graph and executing register transfers. cs max is the control step of the last completion of a register transfer (cs-max is part of rtm). After this control step the values of the registers associated with output variables must implement the values of these output variables. 

The data flow graphs are allowed to contain conditionals as well as loop constructs. A token flow semantics is responsible for a concise behavioral model, without the need for additional external control information. Having conditionals and loops coherently represented in the data flow graph allows synthesis programs to perform several global optimizations, uninhibited by block boundaries, as are imposed by most other representations. 

Figure 11 The control, data flow, and network graphs.

Compilation of the source HDL into an internal representation, usually a data flow graph and/or a control flow graph. This step is very similar to the compilation of a programming language. 

The DSL language, mapping onto a data flow graph, data path synthesis, control step scheduling, a decoder example, a priority encoder example, and the MC6800iO. 

The Value Trace [Snow78] is an internal control and data-flow graph representation of ISPS. This graph represents behavior in terms of operators that correspond to ISPS operators and the values that pass between them. Operators are represented by nodes in the graph. They perform a function on their inputs and produce one or more outputs. Operator inputs and outputs are connected to other operator inputs and outputs by directed edges that represent values. Each value represents an individual value of an ISPS variable or intermediate expression. Since variables may be assigned several values in an ISPS description, there may be several values for each ISPS variable. 

In the absence of a static evaluation of the data flow graph, the probability of control passing through all clusters in a given procedure is simplified to be equivalent. Therefore the probability of any one cluster being activated at any time is approximated by the product of the probability that the procedure it is in is will be activated and the number of clusters in that procedure. Thus if cluster a is in procedure A, the probability of cluster a being activated is estimated by... 

Analyzing conditional mutaul exclusiveness among operations very simply, without analyzing both control flows or data flow graphs. 

The objective function states that we are going to minimize the total cost of the function units. Constraint (1) states that no schedule should have a control step containing more than Mtj, function units of type tk. It is clear that o,- can only be scheduled into a step between Si and Li, which is reflected in (2). Constraint (3) ensures that the precedence relations of the data flow graph (DFG) will be preserved. 

Software-based techniques to detect control flow errors differ from data flow because of one main reason, which is control flow techniques can be optimized. Data flow techniques always have to replicate data (registers, variables or memory positions) and compare it, while control flow techniques can analyze the code, comprehend it and optimize the replication and comparison. Most control flow techniques perform an analysis on the program s execution flow, divide the program irrto Basic Blocks (BB) and parse the program flow as a graph between different nodes (BBs). A BB is defined as a sequence of consecutive instmctions that are always executed sequentially, meaning that the control flow always enters a BB in the first instmctions and leaves at the end of it. 

The interface provides functions to access and manipulate graphs in the three domains (data flow, control, network), and the links between them. The consistency of the data structures is enforced by the interface. 

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. 

The data/control-flow graphs are large and complex. Moreover, the applications require clock frequencies of 10 20 MHz, whereas the intermediate data throughput and sample frequencies remain far below 1 MHz. As a result of these factors, the hardware-sharing factor (HSF) is much larger than 1, resulting in highly multiplexed architectures. 

Scheduling receives the optimized data/control flow graph, a fixed CBB allocation, and a maximum timing constraint as input. The goal of scheduling... 

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. 

Continuing the previous example, separate data-flow and control-flow graphs are generated, as shown in Figure 3. Nodes are numbered with the labels provided in comments in the VHDL model. Operation nodes in the data-flow and control-flow graphs correspond to each other, and are labeled with the operation they represent, e.g., The reader can easily verify the one-to-one correspondence to the specification of EXAMPLE in Figure 2. 

The method described here of separating the data and control flow is only one of a variety of ways that high-level synthesis systems represent this information. Some systems choose to represent the operations only once, mixing both graphs. Other systems preserve only the essential parts of the control flow, such as loops and conditional branches. For example, the ordering of operations 5 and 6 is not relevant, since they are not data dependent, so that ordering does not have to be stored. Unfortunately, such mixed data/control flow representations are also occasionally called data-flow representations, which creates some confusion. For didactical purposes, we have chosen to separate data flow and control flow in this book. 

Reducing the number of levels in the data-flow or control-flow graphs, such as in FLAMEL [63], which may lead to faster hardware. 

The CSTEP control step scheduler uses list scheduling on a block-by-block basis, with timing constraint evaluation as the priority function. Operations are scheduled into control steps one basic block at a time, with the blocks scheduled in executidepth-first traversal of the control flow graph. For each basic block, data ready operator are considered for placement into the current control step, using a priority function that reflects whether or not that placement will violate timing constraints. Resource limits may be applied to limit the number of operators of a particular type in any one control step. 

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