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The demand for biomedical implants keeps increasing. However, most of the current implant design methodologies involve custom-ASIC design. The SiMS project aims to change this process and make implant design more modular, flexible, faster and extensible. The most recent work within the SiMS context provides ImpEDE, a framework based on a multiobjective genetic algorithm, for automatic exploration of the design space of implant processors. The framework provides the processor designer with a Pareto front through which informed decisions can be made about specific implant families after analyzing their particular tradeoffs and requirements. A highly efficient, parallelized version of the genetic algorithm is also used to evolve the front and has as its objectives the optimization of power, performance and area. In addition, we illustrate the extensibility of our framework by modifying it to include a case study of a synthetic implant application with hard realtime deadlines.