Precise control implementation

Precise control implementation. As background for defining a precise control implementation, we describe first our model of the control implementation for the sequencing graph model. We assume a synchronous implementation of control that can be modeled on the whole as a synchronous finite-state machine (FSM) where transitions occur by the ass on of a clock signal at every cycle. The model of synchronous control as a FSM serves as an abstraction to reason about its properties in particular, it does not imply its physical realization in hardware, i.e. the control circuit may be physically implemented either as a single FSM or as a network of FSMs. 

More specifically, given a constraint graph G and a relative schedule 12(G), we define a precise control implementation for G as follows. 

An important characteristic of preciseness is that it is defined with respect to a given schedule. For a constraint graph, there can exist multiple schedules, each of which may be used to define a precise control implementation. If the schedule is minimum, then the corresponding precise control implementation will yield the minimum control delay. In this case, we omit for brevity the reference to the schedule, and simply call a precise control implementation as one that is precise w.r.L the minimum schedule. 

Intuitively, the time to execute a precise control implementation of hardware behavior depends solely on the execution of the operations and not on the transfer of control. This means that the time required by the control to activate a data-dependent delay operation is precisely equal to its execution delay. For example, if an extra cycle is needed to transfer control to a called procedure (as in the microcode-based implementation of [TLW+90]), then the control implementation is not precise by the above definition. On the other hand, a precise... 

For both approaches, we prove that they yield a precise control implementation. We prove first that the ad tive control implementation is precise w.r.t. the minimum schedule in the absence of detailed timing constraints in other words, an operation is activated as soon as all its predecessors have completed execution. We then prove that the relative control implementation is precise w.r.t. a given schedule, regardless of whether the schedule is minimal or non-minimal. 

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. 

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. 

The precise control implementation extends the approach described in the previous section by incorporating the mechanisms of look-ahead resetting and dynamically identifying stateless computations. The control element for a state vertex vi, shown in Figure 8.8, has two states as in the previous model ready Si and wait Sf. The transition conditions are now described as follows ... 

We begin by decomposing the definition of preciseness into two criteria, both of which must be satisfied by a precise control implementation. We say that a control element executes when it activates the corresponding operation, i.e. asserts the activate signal, and a control element completes execution when the corresponding operation completes execution. Let pred,tate( Vi) C V denote the set of state vertices such that a state vertex Vp is in pred,tate vi) if there is a path of stateless vertices from Vp-to-vj. 

Temperature. The process temperature of an evaporative crystallizer may be controlled by the absolute pressure in the vessel. Direct flow control of steam to an ejector, while economizing on steam utilization, is subject to pressure fluctuations due to disturbances in the steam supply pressure. Applying cascaded flow control would decrease the response time of the control loop. Flow control of a bleed gas or exhausted gas into the suction of the vacuum source is the most responsive and precise control option. Critically damped tuning of the pressure control loop should be implemented to prevent rapid temperature changes and high supersaturation generation from fast swings in pressure. 

From the examples presented in this chapter, one can see the extraordinary achievement of NMR QIP. The power of the technique lies in the precise control over the radiofre-quency pulses that implement the quantum logic gates, allowing the manipulation of the coherences and energy level populations. Unwanted effects, usually due to small hardware imperfections, can be corrected during and after the a protocol implementation. 

It stands to reason that, in order to implement the potentiality of silica sols in full measure, much work needs to be done at all stages of their preparation, especially in what concerns precise control over the dimensions of starting hyperbranched polyethoxysilanes and the effect of hydrolysis conditions on the parameters of target silica sols. It is felt, however, that our tentative steps in this direction have met with some success. 

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