Bit of an odd ball thing to post I know, someone might find it useful so last night I open sourced my TigerSharc 201 tool chain.

Its a DSP chip that is relevant in the music world specifically in signal processing, but has many other applications.

This project contains the 3 core components of any chip's toolchain, the Compiler (C to Assembly) the Assembler (Assembly to Machine Objects) and the Linker (Compiles your objects and provides a binary the DSP can run)

http://www.github.com/rm2kdev/TigerSharc-201-Tool-Chain

I built and used this to compile a firmware for a particular bit of kit, unfortunately that project was shut down by the manufacturer (respectfully and politely I'll add) so I pivoted to building my own vst thing instead www.forgeefx.com (not an ad, just part of the true story) since all the code was pure C i just stopped using this compiler, and ported to a vst.

Its highly optimized and produces binaries that run faster than the real compiler, yes, a good bit of AI assistance (still claiming to be an engineer as I've worked in the industry for 20+ years), no reverse engineering used it was created by reading the DSP/CPU's instruction set in the official published documentation (Processor Programming Reference) and cleanly implementing all of those instructions and then building optimizations for the compilation process. (mostly around math, opcode usage, memory etc)

This on its own, does not produce binaries that run on any hardware (That would need a boot loader, and firmware, and other work), it does however produce binaries that run on this exact DSP/CPU in question.

Happy to answer any questions.

Hi everyone,
I work in automotive software and want to build a solid understanding of Image Signal Processing (ISP) for ADAS cameras. I’m new to ISP, and I find vendor documentation difficult to follow without understanding the basics first.
I’m looking for a structured learning path covering:
Camera fundamentals: How image sensors work, RAW images, Bayer patterns, exposure, gain, and noise.
ISP pipeline: Demosaicing, white balance, noise reduction, color correction, HDR, and tone mapping.
ISP tuning: How to evaluate image quality and understand the trade-offs between different settings.
ADAS applications: How ISP settings affect object detection and perception, especially in low light or high-contrast scenes.
Which courses, books, YouTube playlists, or practical tutorials would you recommend? Free and paid options are both welcome. Hands-on exercises using RAW images, Python, or OpenCV would be especially helpful.
If you work in automotive imaging, where would you start, and in what order would you learn these topics? Please share links or resource names.
Thanks!