Design Space Exploration Strategy

With the design space formulated as a set of resource bindings for a given resource allocation, Hebe explores the design space to find a favorable implementation with respect to a particular design goal, such as minimal area ot minimal latency. Any valid implementation must satisfy both resource and timing constraints. 

Using the conflict-free allocation as guideline, a set of resource allocations ai.afc is specified either by the user manually or by the system automatically. Hebe supports both exact and heuristic strategies to explore the design space they are summarized below. 

The remainder of this chapter describes each strategy in detail. 

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Before describing the design space exploration strategy, we introduce first the concept of concurrency factor to measure of the degree of parallelism among subsets of shareable operations Q Q V. This concept is used extensively in resource allocation and heuristic exploration of the design space. 

Pruning ensures that pruned allocations or bindings always violate the required timing constraints. However, many bindings may still result even after pruning. This leads to the heuristic design space exploration strategy, described in the next section. 

Figure 5.11 Block diagram of subspace generation strategy for heuristic design space exploration.

Figure 5.13 Block diagram of the design space ranking strategy in heuristic design space exploration.

The heuristic design space exploration and conflict resolution strategy was... 

Whilst salts of active pharmaceutical ingredients (APIs) are restricted to (acceptable) counter-ions, the potentially available pharmaceutical space around co-crystals is much broader. Indeed the list of co-formers is long and the API (even if un-ionized) can theoretically be co-crystallized either with acidic, basic or neutral co-formers in multiple combinations thanks to a more adaptive stoichiometry. As one can easily foresee the type of advantageous properties for a solid form that may be accessed in such enlarged space, it is of paramount importance for the pharmaceutical stakeholder to design an efficient strategy for exploring it. 

For some designs, exact exploration of the design space may be prohibitive due to its size. Hebe also supports heuristic strategies to explore the design space, where the resulting implementation is no longer guaranteed to be optimal. 

The ability to recognize such interaction effects is therefore very important when a new reaction is elaborated, otherwise the potential of the reaction for preparative use may be overlooked. It is therefore necessary to use multivariate strategies to explore the reaction space so that the joint influence of varying the substrate, the reagent(s), and the solvent can be evaluated. This can be accomplished by multivariate designs in the principal properties. These designs will define sub-sets of test systems which can furnish the desired information. The principles are discussed in Chapter 16. 

Tabu search provides a technique for exploring the solution space beyond points where traditional approaches become trapped at a local optimum. Tabu search does not supplant these traditional approaches. Instead, it is designed as a higher-level strategy that guides their application. 

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