Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A processor's performance and por consumption are tied; an increased performance demands more por, and vice versa. An optimal tradeoff can only be achieved by an improved prediction of the task execution times, prior to an efficient scheduling. Moreover, since the processor's soft error rate is a function of its operating voltage, it is also linked to the performance-por trade-off. The situation is further complicated for the case of multicore architectures where the tasks are to be mapped on separate cores (processing elements).  proposes a joint State-Space model to achieve improved task execution time estimation, leading to better scheduling for optimizing the trade-off, particularly in the context of multicore soft real-time systems. It does not assume any `a priori' knowledge about the task graph or its properties, and is independent of the underlying architecture. It learns the system dynamics over time. The state-space solution is formulated using a recursive implementation of the online Monte Carlo Method. Having obtained the estimates of the execution times, they are compensated for the soft error according to a given soft error rate. At the beginning of each scheduling interval, the low por EDF scheduling decision is carried out to execute the tasks. The proposed method (SEAL) achieves 29% better energy savings compared to state-of-the-art, while the deadline misses are under 7% without the loss of system failure probability. The results obtained clearly show the advantage in terms of energy savings.
['Nabeel Iqbal', 'Muhammad Adnan Siddique', 'Jörg Henkel']
I am writing a research paper on a idea SEAL: soft error aware low power scheduling by Monte Carlo state space under the influence of stochastic spatial and temporal dependencies
[{"from": "human", "value": "I am writing a research paper on a idea SEAL: soft error aware low power scheduling by Monte Carlo state space under the influence of stochastic spatial and temporal dependencies"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A processor's performance and por consumption are tied; an increased performance demands more por, and vice versa. An optimal tradeoff can only be achieved by an improved prediction of the task execution times, prior to an efficient scheduling. Moreover, since the processor's soft error rate is a function of its operating voltage, it is also linked to the performance-por trade-off. The situation is further complicated for the case of multicore architectures where the tasks are to be mapped on separate cores (processing elements).  proposes a joint State-Space model to achieve improved task execution time estimation, leading to better scheduling for optimizing the trade-off, particularly in the context of multicore soft real-time systems. It does not assume any `a priori' knowledge about the task graph or its properties, and is independent of the underlying architecture. It learns the system dynamics over time. The state-space solution is formulated using a recursive implementation of the online Monte Carlo Method. Having obtained the estimates of the execution times, they are compensated for the soft error according to a given soft error rate. At the beginning of each scheduling interval, the low por EDF scheduling decision is carried out to execute the tasks. The proposed method (SEAL) achieves 29% better energy savings compared to state-of-the-art, while the deadline misses are under 7% without the loss of system failure probability. The results obtained clearly show the advantage in terms of energy savings."}]