CASPER: An Integrated Energy-Driven Approach for Task Graph Scheduling on Distributed Embedded Systems

Dataflow Model–based Software Synthesis Framework for Parallel and Distributed Embedded Systems

ACM Transactions on Design Automation of Electronic Systems ◽

10.1145/3447680 ◽

2021 ◽

Vol 26 (5) ◽

pp. 1-38

Author(s):

Eunjin Jeong ◽

Dowhan Jeong ◽

Soonhoi Ha

Keyword(s):

Embedded Systems ◽

Software Development ◽

Synthesis Method ◽

Real Life ◽

Task Graph ◽

Software Synthesis ◽

Distributed Embedded Systems ◽

Hardware Platform ◽

Processing Elements ◽

Model Based

Existing software development methodologies mostly assume that an application runs on a single device without concern about the non-functional requirements of an embedded system such as latency and resource consumption. Besides, embedded software is usually developed after the hardware platform is determined, since a non-negligible portion of the code depends on the hardware platform. In this article, we present a novel model-based software synthesis framework for parallel and distributed embedded systems. An application is specified as a set of tasks with the given rules for execution and communication. Having such rules enables us to perform static analysis to check some software errors at compile-time to reduce the verification difficulty. Platform-specific programs are synthesized automatically after the mapping of tasks onto processing elements is determined. The proposed framework is expandable to support new hardware platforms easily. The proposed communication code synthesis method is extensible and flexible to support various communication methods between devices. In addition, the fault-tolerant feature can be added by modifying the task graph automatically according to the selected fault-tolerance configurations by the user. The viability of the proposed software development methodology is evaluated with a real-life surveillance application that runs on six processing elements.

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Sensitivity-driven co-synthesis of distributed embedded systems

Proceedings of the 8th international symposium on System synthesis - ISSS '95 ◽

10.1145/224486.224488 ◽

1995 ◽

Cited By ~ 4

Author(s):

Ti-Yen Yen ◽

Wayne Wolf

Keyword(s):

Embedded Systems ◽

Distributed Embedded Systems

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MODES: model-based optimization on distributed embedded systems

Machine Learning ◽

10.1007/s10994-021-06014-6 ◽

2021 ◽

Author(s):

Junjie Shi ◽

Jiang Bian ◽

Jakob Richter ◽

Kuan-Hsun Chen ◽

Jörg Rahnenführer ◽

...

Keyword(s):

Machine Learning ◽

Embedded Systems ◽

Learning Model ◽

Black Box ◽

Distributed Embedded Systems ◽

Data Set ◽

Individual Model ◽

Model Based ◽

Machine Learning Model ◽

Distributed Machine Learning

AbstractThe predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework $$\textit{MODES}$$ MODES that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) $$\textit{MODES}$$ MODES -B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) $$\textit{MODES}$$ MODES -I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate $$\textit{MODES}$$ MODES by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy ($$\textit{MODES}$$ MODES -B), run-time efficiency ($$\textit{MODES}$$ MODES -I), and statistical stability for both modes, $$\textit{MODES}$$ MODES outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set.

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