Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The increasing demand for low por and high performance multimedia embedded systems has motivatedation bandwidth and latency requirements under a tight por budge the need for effective solutions to satisfy applict. As technology scales, it is imperative that applications are optimized to take full advantage of the underlying resources and meet both por and performance requirements.  propose a methodology capable of discovering and enabling parallelism opportunities via code transformations, efficiently distributing the computational load across resources, and minimizing unnecessary data transfers. Our approach decomposes the application's tasks into smaller units of computations called kernels, which are distributed and pipelined across the different processing resources.  exploit the ideas of inter-kernel data reuse to minimize unnecessary data transfers beten kernels and early execution edges to drive performance. Our experimental results on a JPEG2000 case study show up to 80% performance improvement and 60% dynamic por reduction over standard application mapping approaches.
['Luis Angel D. Bathen', 'Yongjin Ahn', 'Nikil Dutt', 'Sudeep Pasricha']
I am writing a research paper on a idea Inter-kernel data reuse and pipelining on chip-multiprocessors for multimedia applications
[{"from": "human", "value": "I am writing a research paper on a idea Inter-kernel data reuse and pipelining on chip-multiprocessors for multimedia applications"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The increasing demand for low por and high performance multimedia embedded systems has motivatedation bandwidth and latency requirements under a tight por budge the need for effective solutions to satisfy applict. As technology scales, it is imperative that applications are optimized to take full advantage of the underlying resources and meet both por and performance requirements.  propose a methodology capable of discovering and enabling parallelism opportunities via code transformations, efficiently distributing the computational load across resources, and minimizing unnecessary data transfers. Our approach decomposes the application's tasks into smaller units of computations called kernels, which are distributed and pipelined across the different processing resources.  exploit the ideas of inter-kernel data reuse to minimize unnecessary data transfers beten kernels and early execution edges to drive performance. Our experimental results on a JPEG2000 case study show up to 80% performance improvement and 60% dynamic por reduction over standard application mapping approaches."}]