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MOODS (Multiple Objective Optimisation in Data and control path Synthesis) is a Behavioural Synthesis System which can automatically generate a number of structural descriptions of a digital circuit from just a single behavioural one. Although each structural description is functionally equivalent to the next, it will have different properties, such as circuit area or delay. The final structural description selected will be the one which best meets the user's optimisation goals and constraints. Run-time Reconfigurable systems operate through multiple configurations of the programmable hardware on which they are implemented, dynamically allocating resources 'on the fly' during their execution. The partially reconfigurable devices upon which they are based, enable area of their configuration memory to be rewritten, without disturbing the operation of existing configurations - unless so desired. This characteristic may be exploited by partitioning a circuit into a number of distinct temporal contexts, which when ultimately realised as device-level configurations may be swapped in and out the device's configuration memory, as the run-time operation of the circuit dictates. At any point during the execution of the temporally partitioned circuit, the area required to implement it is equal to the size of the largest context and not the sum of each of its constituent parts, as would be the case in a non-reconfigurable implementation. The reduction in circuit area comes at the cost of a reconfiguration overhead, the time taken to partially reconfigure the device with each configuration and the frequency at which this form of context switching occurs. This Thesis describes an extension to the original MOODS system, enabling it to quantify the trade-off that exists between the potential area reduction offered through run-time reconfiguration and the subsequent reconfiguration overhead incurred as a result. In addition to performing the temporal partitioning alongside existing circuit optimisation, MOODS is now able to automatically generate the infrastructure to support a practical implantation of the temporal contexts on a commercial Field Programmable Gate Array.