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Current technology trends indicate that power- and energy efficiency will limit chip throughput in the future. Current solutions to these problems, either in the way of programmable or fixed-function digital accelerators will soon reach their limits as microarchitectural overheads are successively trimmed. A significant departure from current computing methods is required to carry forward computing advances beyond digital accelerators. In this paper we describe how the energy-efficiency of a large class of problems can be improved by employing a hybrid of the discrete and continuous models of computation instead of the ubiquitous, traditional discrete model of computation. We present preliminary analysis of domains and benchmarks that can be accelerated with the new model. Analysis shows that machine learning, physics and up to one-third of SPEC, RMS and Berkeley suite of applications can be accelerated with the new hybrid model.