Master Thesis Student Hardware Acceleration

Hardware acceleration of algorithms for motion control of autonomous heavy vehicles
Background
Motion control for autonomous vehicles is used to control the driving dynamics of the whole vehicle. It has the task to control all the available actuators in a vehicle to safely and efficiently follow the intended path along the road. Many of the software algorithms used are computational intense and must also fulfill real-time deadlines. The main resource used to run the algorithms is a central processing unit, which is also used for most of the other tasks in the system. A way to increase the computational capacity without comprising the performance of the complete system might be to off-load some algorithms to special purpose hardware, and run them in parallel with the ordinary program flow. This is called hardware acceleration. Examples of such a special purpose hardware is a DSP (Digital Signal Processor), FPGA (Field-Programmable Gate Array), or GPGPU (General Purpose Graphical Processing Unit).
Objective
The objective of the thesis is to find algorithms in the concerned Volvo system that are suitable for hardware acceleration, and port those algorithms to special purpose hardware. Moving to a heterogeneous computer environment will have many system levels impacts, for instance concerning determinism, functional safety, debugging and tool support. This should be considered throughout the thesis work, and be an important factor when deciding the overall suitability of using hardware acceleration for a particular algorithm.
Scope and Method
Firstly, a literature survey about hardware accelerations methods needs to be conducted. Particular focus should be on hardware that is used in the Volvo systems. The literature study should also include algorithms aspects and what type of algorithms that are suitable for different kinds of acceleration methods. After this first step, suitable algorithms from the Volvo system is to be selected and ported to the selected hardware acceleration method. These algorithms should then be integrated in the complete system and a thorough analysis of the performance and other systems aspects should be performed.
The scope can be adapted to one or two students. Suitable background is computer science or similar, with an interest for embedded systems.
Thesis Level: Master
Language: English
Starting date: January 2020
Number of students: 1-2
Tutor
Christoffer Markusson, +46 73 902 68 83

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The Volvo Group is one of the world’s leading manufacturers of trucks, buses, construction equipment and marine and industrial engines under the leading brands Volvo, Renault Trucks, Mack, UD Trucks, Eicher, SDLG, Terex Trucks, Prevost, Nova Bus, UD Bus and Volvo Penta.

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