Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The in situ processing of vast amounts of data, available intermittently in networks of sensors, motivates investigation of means for achieving high performance when required, but ultralow-por dissipation when idle. One approach is the use of embedded multiprocessor systems, leading to tradeoffs beten parallelism, performance, energy-efficiency, and cost. To evaluate these tradeoffs, and to gain insight for future system designs, this letter presents the design, implementation, and evaluation of a miniature, energy-scalable, 24-processor module, L24, for compute-intensive in situ sensor data processing tasks. The platform provides idle por dissipation over an order of magnitude lor than systems employing a monolithic processor of equivalent performance, while dynamic por dissipation remains competitive. Taking into account both application computation and interprocessor communication demands, it is shown that there may exist an optimum operating voltage that minimizes either time-to-solution, energy usage, or the energy-delay product. This optimum operating point is formulated analytically, calibrated with system measurements and instruction-level microarchitectural simulation, and evaluated for the hardware platform and application presented.
['Phillip Stanley-Marbell']
I am writing a research paper on a idea Parallelism, Performance, and Energy-Efficiency Tradeoffs for In Situ Sensor Data Processing
[{"from": "human", "value": "I am writing a research paper on a idea Parallelism, Performance, and Energy-Efficiency Tradeoffs for In Situ Sensor Data Processing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The in situ processing of vast amounts of data, available intermittently in networks of sensors, motivates investigation of means for achieving high performance when required, but ultralow-por dissipation when idle. One approach is the use of embedded multiprocessor systems, leading to tradeoffs beten parallelism, performance, energy-efficiency, and cost. To evaluate these tradeoffs, and to gain insight for future system designs, this letter presents the design, implementation, and evaluation of a miniature, energy-scalable, 24-processor module, L24, for compute-intensive in situ sensor data processing tasks. The platform provides idle por dissipation over an order of magnitude lor than systems employing a monolithic processor of equivalent performance, while dynamic por dissipation remains competitive. Taking into account both application computation and interprocessor communication demands, it is shown that there may exist an optimum operating voltage that minimizes either time-to-solution, energy usage, or the energy-delay product. This optimum operating point is formulated analytically, calibrated with system measurements and instruction-level microarchitectural simulation, and evaluated for the hardware platform and application presented."}]