Cooperative Game Theory within Multi-Agent Systems for Systems Scheduling

Research concerning organization and coordination within multi-agent systems continues to draw from a variety of architectures and methodologies. The work presented in this paper combines techniques from game theory and multi-agent systems to produce self-organizing, polymorphic, lightweight, embedded agents for systems scheduling within a large-scale real-time systems environment. Results show how this approach is used to experimentally produce optimum real-time scheduling through the emergent behavior of thousands of agents. These results are obtained using a SWARM simulation of systems scheduling within a High Energy Physics experiment consisting of 2500 digital signal processors.Comment: Fourth International Conference on Hybrid Intelligent Systems (HIS), Kitakyushu, Japan, December, 200

The Chameleon Architecture for Streaming DSP Applications

We focus on architectures for streaming DSP applications such as wireless baseband processing and image processing. We aim at a single generic architecture that is capable of dealing with different DSP applications. This architecture has to be energy efficient and fault tolerant. We introduce a heterogeneous tiled architecture and present the details of a domain-specific reconfigurable tile processor called Montium. This reconfigurable processor has a small footprint (1.8 mm$^2$ in a 130 nm process), is power efficient and exploits the locality of reference principle. Reconfiguring the device is very fast, for example, loading the coefficients for a 200 tap FIR filter is done within 80 clock cycles. The tiles on the tiled architecture are connected to a Network-on-Chip (NoC) via a network interface (NI). Two NoCs have been developed: a packet-switched and a circuit-switched version. Both provide two types of services: guaranteed throughput (GT) and best effort (BE). For both NoCs estimates of power consumption are presented. The NI synchronizes data transfers, configures and starts/stops the tile processor. For dynamically mapping applications onto the tiled architecture, we introduce a run-time mapping tool

DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs

In addition to General Purpose Processors (GPP), Multicore SoCs equipping modern mobile devices contain specialized Digital Signal Processor designed with the aim to provide better performance and low energy consumption properties. However, the experimental measurements we have achieved revealed that system overhead, in case of DSP video decoding, causes drastic performances drop and energy efficiency as compared to the GPP decoding. This paper describes DyPS, a new approach for energy-aware processor switching (GPP or DSP) according to the video quality . We show the pertinence of our solution in the context of adaptive video decoding and describe an implementation on an embedded Linux operating system with the help of the GStreamer framework. A simple case study showed that DyPS achieves 30% energy saving while sustaining the decoding performanc

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

The Chameleon project in retrospective

In this paper we describe in retrospective the main results of a four year project, called Chameleon. As part of this project we developed a coarse-grained reconfigurable core for DSP algorithms in wireless devices denoted MONTIUM. After presenting the main achievements within this project we present the lessons learned from this project