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Some more related info on different algorithmic differentiation approaches in Julia: https://github.com/MikeInnes/diff-zoo


Thank you so much for sharing this great repo! I have noticed that the source transformation notebook is not finished yet. How is it now?


A RISC-V linux system for the ECP5 can be built with the FOSS toolchain using vexriscv and litex:

https://github.com/litex-hub/linux-on-litex-vexriscv

also, step by step using a different core (rocket): https://insights.sei.cmu.edu/sei_blog/2019/10/how-to-build-a...


I'm amazed to see the preferred toolchain for LiteX for ECP5 listed as "Yosys/Trellis/Nextpnr" (and vendor proprietary tools for the other families).

The rocket core mentions 102% of space used of an 85K LUT FPGA and having to deal with it. I wonder which core is using so much space? I have more experience with vendor soft cores, and they would use much less..


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Oh wow. This is basically a paper about astrologists. Was it published under astrology or anthropology?



I messed around with some slightly above toy work, implementing expectation-maximization for gaussian mixture models and some other supporting tools for 3D point clouds, and found it really intuitive for someone with a decent amount of FP experience but little GPU experience.

Biggest challenge for me was a lack of profiling tools when running OpenCL on an NVIDIA GPU, I got OK performance but had no way to identify hotspots or opportunities for improvement.



If anyone has AIAA paper access (or a workaround ;), they published a ton of details on their guidance, control, and avionics designs at the beginning of this year:

https://arc.aiaa.org/doi/10.2514/6.2018-0023 https://arc.aiaa.org/doi/abs/10.2514/6.2018-1849


check ntrs?


Joe Grand developed a similar product for Parallax a while back, with a lot of interesting technical details on his site: http://www.grandideastudio.com/laser-range-finder/


The laser range finder you linked to is different than the OP's 360 degree scanning LIDAR (... as the former doesn't scan!) :-)


#1 is not strictly true, although it's not necessarily useful yet: https://github.com/japaric/nvptx#nvptx


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