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Due to the exponentially increasing size of design space of microprocessors and time-consuming simulations, predictive models have been widely employed in design space exploration (DSE). Traditional approaches mostly build a program-specific predictor that needs a large number of program-specific samples. Thus considerable simulation cost is required for each program. In this paper, we study the novel...
Design space exploration (DSE) has become a notoriously difficult problem due to the exponentially increasing size of design space of microprocessors and time-consuming simulations. To address this issue, machine learning techniques have been widely employed to build predictive models. However, most previous approaches randomly sample the training set leading to considerable simulation cost and low...
In this paper, we propose C2-Bound, a data-driven analytical model, that incorporates both memory capacity and data access concurrency factors to optimize many-core design. C2-Bound is characterized by combining the newly proposed latency model, concurrent average memory access time (C-AMAT), with the well-known memory-bounded speedup model (Sun-Ni's law) to facilitate computing tasks. Compared to...
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