Precise adaptive control implementation

A limitation of the simple control implementation described in the previous section is that it may not be precise. An inherent difficulty lies in the fact that resetting of the control network occurs too late in time. There is a conflict between trying to reset the network in preparation for the next activation and actually knowing when to reset 

To illustrate the difficulty, consider a sequencing graph G, representing either a process or a loop body that we wish to execute repeatedly. At a particular cycle n, is in its final cycle of execution and donea is asserted at the start of the next cycle n -i- 1. Ideally, the control of G, should be ready to restart 

When all operations have fixed delays, the restarting periodicity can be hardcoded into the control because the latency of the graph is fixed. We say in this case that the control equations can be statically derived. Conv sely, when data-dependent delay operations are present, the control equations have to consider dynamically variations in the input signals. Since the latency of the graph may change, the hard-coded control approach cannot be used in gen. To resolve this difficulty, two mechanisms are used to construct a precise control implementation. The first mechanism is to use lookahead to ensure prqrer resetting of the control fca all input sequences. The second mechanism is to dynamically identify stateless operations. 

A complication arises since the execution delay of a data-dependent vertex may change for different input sequences. In particular, the delay may become zero, making the vertex stateless. Therefore, it is impossible to always statically 

With this formulation, the control can track the variations in the execution delay of a graph because the dsink signals are evaluated dynamically during hardware execution. When the direct-sink vertices are all bounded vertices, the dsink signals can be evaluated statically, not adding to the complexity of the resulting control implementation. 

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Lemma 8.U The precise adaptive control implementation of G, satisfies the precise transition criterion. 

Detailed timing constraints are not considered in the adaptive control scheme. In this case, the minimum control delay for any control implementation of G, is simply the length of the longest weighted path in G, from the source vertex Vo to the sink vertex v , where the weight of a vertex is equal to its execution delay for that particular input sequence. We will show that the adaptive control implementation is precise by guaranteeing its control delay to be minimum for all input sequences. Extensions to support detailed timing constraints are presented in the next section. 

The control network adapts to the changing execution delays of the operations. It has several advantages that include modularity, distribution of control, uniform handling of both fixed and data-dependent delay operations, and support for multiple concurrent execution flows. We describe now a simple adaptive control implementation that satisfies these requirements. Although it may not be precise in terms of control delay, we use this simple model to justify a more elaborate control scheme that is presented in the next section which satisfies the preciseness requirement... 

We now prove the adaptive control implementation in the previous section to be precise. 

Proof For a given input sequence, the adaptive control implementation in the previous section satisfies both the precise transition (Lemma 8.1.3) and precise restarting (Lemma 8.1.4) criteria. By Lemma 8.1.1, the control delay is equal to the latency of the sequencing graph for a given input sequence. 

The categorization of vertices to stateless versus state and direct-sink versus indirect-sink is dynamically evaluated through the use of the stateless and dsink signals for each control element. Therefore, Lemma 8.1.3 and Lemma 8.1.4 hold for all input sequences. Since Lemmas 8.1.1 is satisfied for all input sequences, the adaptive control implementation is precise. ... 

We present an overview of the basic strategy in Section 8.1.1. Two control implementations are presented. Section 8.1.2 describes a simplified scheme that supports data-dependent delay operations and multiple execution flows, but the resulting control is not precise. We extend the simplified scheme in Section 8.1.3 to obtain a precise control implementation. Analysis of adaptive control is presented in Section 8.1.4. 

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To achieve the required quality, the chemist should be involved from the beginning of the process, when the needs of the users of results are defined, until the final report is delivered. In practice, therefore, the analytical chemist has to be consulted at every stage of the process sample selection, sample storage, and transport procedures, the parameters to be analyzed, and the level of accuracy and precision necessary for an adequate response to be given. This will enable the analyst to set up a scientifically and economically adapted and accepted measurement procedure for the intended purpose as required by the QA system. Moreover, implementation of this system must guarantee that all necessary QC measures can be anticipated, so that the entire quality cycle is under control.4... 

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