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

Modelling Heterogeneous DSP–FPGA Based System Partitioning with Extensions to the Spinach Simulation Environment

In this paper we present system-on-a-chip extensions to the Spinach simulation environment for rapidly prototyping heterogeneous DSP/FPGA based architectures, specifically in the embedded domain. This infrastructure has been successfully used to model systems varying from multiprocessor gigabit ethernet controllers to Texas Instruments C6x series DSP based systems with tightly coupled FPGA based coprocessors for computational offloading. As an illustrative example of this toolsets functionality, we investigate workload partitioning in heterogeneous DSP/FPGA based embedded environments. Specifically, we focus on computational offloading of matrix multiplication kernels across DSP/FPGA based embedded architectures

Experimental Approaches to the Composition of Interactive Video Game Music

This project explores experimental approaches and strategies to the composition of interactive music for the medium of video games. Whilst music in video games has not enjoyed the technological progress that other aspects of the software have received, budgets expand and incomes from releases grow. Music is now arguably less interactive than it was in the 1990’s, and whilst graphics occupy large amounts of resources and development time, audio does not garner the same attention. This portfolio develops strategies and audio engines, creating music using the techniques of aleatoric composition, real-time remixing of existing work, and generative synthesisers. The project created music for three ‘open-form’ games : an example of the racing genre (Kart Racing Pro); an arena-based first-person shooter (Counter-Strike : Source); and a real-time strategy title (0 A.D.). These games represent a cross-section of ‘sandbox’- type games on the market, as well as all being examples of games with open-ended or open-source code

Three architectures for volume rendering

Volume rendering is a key technique in scientific visualization that lends itself to significant exploitable parallelism. The high computational demands of real-time volume rendering and continued technological advances in the area of VLSI give impetus to the development of special-purpose volume rendering architectures. This paper presents and characterizes three recently developed volume rendering engines which are based on the ray-casting method. A taxonomy of the algorithmic variants of ray-casting and details of each ray-casting architecture are discussed. The paper then compares the machine features and provides an outlook on future developments in the area of volume rendering hardware