BeagleBoard/GSoC/2021 Proposal/ALSA plugin for BELA
[ALSA plugin for BELA]
About
Student: Jakub Duchniewicz
Mentors: Giulio Moro
Code: not yet created!
Wiki: https://elinux.org/index.php?title=BeagleBoard/GSoC/2021_Proposal/GPGPU_with_GLES
GSoC: [1]
Status
Discussing the implementation ideas with Giulio Moro and others on #beagle-gsoc IRC.
About you
IRC: jduchniewicz
Github: JDuchniewicz
School: University of Turku/KTH Royal Institute of Technology
Country: Finland/Sweden/Poland
Primary language: Polish
Typical work hours: 8AM-5PM CET
Previous GSoC participation: Participating in GSoC, especially with BeagleBoard would further develop my software and hardware skills and help me apply my current knowledge for the mutual benefit of the open source community. I planned to do the YOLO project, but after spending several days researching and preparing the proposal I found it is impossible to do on current BBAI/X15.
About your project
Project name: ALSA plugin for BELA
Description
// todo: add links BELA is a cape designed for BB Black which features real-time audio processing via usage of Xenomai threads. Apart from being a hardware solution, BELA supplies its own operating system based on Debian Linux distribution and a full-fledged IDE allowing for seamless audio development experience. BELA provides its own library for interfacing with the hardware, however it does not provide any unified interface via ALSA, JACK or PulseAudio. Therefore, it is currently impossible to use BELA like a regular Linux audio device and it has to be done by utilizing its API calls.
The main premise of this project is to enable the unified access by means of ALSA plugin. This plugin will allow for tying user-provided functions for regular system calls alsa-lib API uses for operating on its devices. Since such need may arise for any other real-time ALSA devices, this plugin would be a valuable addition to the ALSA ecosystem and would be mainline'able. This way, users can call regular ALSA API's for interacting with the device and still profit from all the real-time benefits it offers.
// add graphs // add the analog etc interpretation stuff?
Implementation
In order to understand why Because the Xenomai thread runs with a higher priority than any other Linux thread,
Alternative ideas/Stretch goals
API Overview
Detailed Implementation
Expected Performance
Action Items
Deliverables
Timeline
During 25.07.21-08.08.21 I have a summer camp from my study programme and will be probably occupied for a half of the day. The camp will most likely be held online though.
| Date | Milestone | Action Items |
|---|---|---|
| 13.04.21-17.05.21 | Pre-work |
|
| 18.05.21-07.06.21 | Community Bonding |
|
| 14.06.21 | Milestone #1 |
|
| 21.06.21 | Milestone #2 |
|
| 28.06.21 | Milestone #3 |
|
| 5.07.21 | Milestone #4 |
|
| 12.07.21 | Milestone #5 |
|
| 19.07.21 | Milestone #6 |
|
| 26.07.21 | Milestone #7 |
|
| 31.07.21 | Milestone #8 |
|
| 7.8.21 | Milestone #9 |
|
| 14.8.21 | Milestone #10 |
|
| 24.08.21 | Feedback time |
|
| 31.08.21 | Results announced |
|
Experience and approach
I have strong programming background in the area of embedded Linux/operating systems as a Junior Software Engineer in Samsung Electronics during December 2017-March 2020. Additionally I have developed a game engine (| PolyEngine) in C++ during this time and gave some talks on modern C++ during my time as a Vice-President of Game Development Student Group "Polygon".
Apart from that, I have completed my Bachelors degree at Warsaw University of Technology successfully defending my thesis titled: | FPGA Based Hardware Accelerator for Musical Synthesis for Linux System. In this system I created a polyphonic musical synthesizer capable of producing various waveforms in Verilog code and deployed it on a De0 Nano SoC FPGA. Additionally I wrote two kernel drivers - one encompassed ALSA sound device and was responsible for proper synchronization of DMA transfers.
I am familiar with Deep Learning concepts and basics of Computer Vision. During my studies at UTU I achieved the maximal grades for my subjects, excelling at Navigation Systems for Robotics and Hardware accelerators for AI.
I have some experience working with OpenGL, mostly learning it for the programming engine needs and for personal benefit. Since this project does not require in-depth knowledge of it, but rather to create an abstraction over the OpenGL ES bindings and perform the necessary data conversions and extractions inside the API. This requires skills I do already possess and have proficiency using.
In my professional work, many times I had to complete various tasks under time pressure and choose the proper task scoping. Basing on this experience I believe that this task is deliverable in the mentioned time-frame.
Contingency
Since I am used to tackling seemingly insurmountable challenges, I will first of all keep calm and try to come up with alternative approach if I get stuck along the way. The internet is a vast ocean of knowledge and time and again I received help from benevolent strangers from reddit or other forums. Since I believe that humans are species, which solve problems in the best way collaboratively, I will contact #beagle, #beagle-gsoc and relevant subreddits (I received tremendous help on /r/FPGA, /r/embedded and /r/askelectronics in the past).
If all fails I may be able be forced to change my approach and backtrack, but this will not be a big problem, because the knowledge won't be lost and it will only make my future approaches better. Alternatively, I can focus on documenting my progress in a form of blogposts and videos while waiting for my mentor to come back to cyberspace.
Benefit
Misc
The qualification PR is available here.
