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User provides a sequential program to parallelize, a function to measure program's output distortions, representative inputs, and goal and constraints. The compiler parallelizes the code, including in it knobs and knob controllers. The profilers annotate knob controllers by using the training inputs. The tuners customize knob controllers for the target platform. While the program runs with the reference input, the runtime interprets knob controllers, adjusting knobs to alleviate performance bottlenecks, while respecting the constraints and goal set.
Source publication
Resources such as quantities of transistors and memory, the level of integration and the speed of components have increased dramatically over the years. Even though the technologies have improved, we continue to apply outdated approaches to our use of these resources. Key computer science abstractions have not changed since the 1960’s. Therefore th...
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Citations
... Many commercial smartphone applications running activity recognition tasks compute these in the server side [13]-which makes this task independent of the wearable device but, on the contrary, demands frequent wireless data transmission. Offloading data from nodes to Cloud servers remains into the Mobile Cloud Computing (MCC) [14] and Remote Health Monitoring (RHM) paradigms, leading to high energy and economic savings [15]. In this paper we focus on the scenario i (one Plug&Play sensing node and a smartphone as back-end). ...
Activity recognition, as an important component of behavioral monitoring and intervention, has attracted enormous attention, especially in Mobile Cloud Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently resource constrained wearable devices have been gaining popularity, their battery life is limited and constrained by the frequent wireless transmission of data to more computationally powerful back-ends. This paper proposes an ultra-low power activity recognition system using a novel adaptive compressed sensing technique that aims to minimize transmission costs. Coarse-grained on-body sensor localization and unsupervised clustering modules are devised to autonomously reconfigure the compressed sensing module for further power saving. We perform a thorough heuristic optimization using Grammatical Evolution (GE) to ensure minimal computation overhead of the proposed methodology. Our evaluation on a real-world dataset and a low power wearable sensing node demonstrates that our approach can reduce the energy consumption of the wireless data transmission up to 81.2% and 61.5%, with up to 60.6% and 35.0% overall power savings in comparison with baseline and a naive state-of-the-art approaches, respectively. These solutions lead to an average activity recognition accuracy of 89.0% only 4.8% less than the baseline accuracy-while having a negligible energy overhead of on-node computation.
