Also see this short post about SIMD on ESP32-S3, discussed previously. https://bitbanksoftware.blogspot.com/2024/01/surprise-esp32-...
SIMD-accelerated computer vision on a $2 microcontroller
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Re: SIMD-accelerated computer vision on a $2 microcontroller
#12I wonder if ESP32 has VLIW slots and a tighter instruction packaging is possible?
Neither Xtensa nor RISC-V are VLIW architectures.
Xtensa architecture is flexible and extendable by the user. Ability to define new instructions, hw features and VLIW configurations are some of the key features. You can find more details on the internet https://en.m.wikipedia.org/wiki/Tensilica
Re: SIMD-accelerated computer vision on a $2 microcontroller
#13We prefer something more expensive and better: https://up-board.org/upsquared/specifications/
Intel UpSquared
Re: SIMD-accelerated computer vision on a $2 microcontroller
#14A comparable board is the ESP32-CAM, which is supported by this really practical computer vision project: https://github.com/jomjol/AI-on-the-edge-device?tab=readme-o...
In the CV department, I recently ordered a cheap FPGA + ARM Cortex-M3 + 64 Mbit SRAM + 32 Mbit flash that does camera input and HDMI output. Like a budget Zynq for CV. https://wiki.sipeed.com/hardware/en/tang/Tang-Nano-4K/Nano-4... https://www.aliexpress.us/item/3256806880637138.html
Cool board!
Would any of the "retro" game/home computer firmwares fit in that FPGA? I find comparing capacity hard for stuff like that.
Re: SIMD-accelerated computer vision on a $2 microcontroller
#15Earlier quoted context omitted.
In the CV department, I recently ordered a cheap FPGA + ARM Cortex-M3 + 64 Mbit SRAM + 32 Mbit flash that does camera input and HDMI output. Like a budget Zynq for CV. https://wiki.sipeed.com/hardware/en/tang/Tang-Nano-4K/Nano-4... https://www.aliexpress.us/item/3256806880637138.html
Cool board! Would any of the "retro" game/home computer firmwares fit in that FPGA? I find comparing capacity hard for stuff like that.
There's absolutely no reason ROMs have to waste scarce resources of a hybrid FPGA. Micro SD cards (called TF in China) and eMMC are the usual solutions.
Example: https://www.aliexpress.us/item/3256806498688867.html
Re: SIMD-accelerated computer vision on a $2 microcontroller
#16Earlier quoted context omitted.
In the CV department, I recently ordered a cheap FPGA + ARM Cortex-M3 + 64 Mbit SRAM + 32 Mbit flash that does camera input and HDMI output. Like a budget Zynq for CV. https://wiki.sipeed.com/hardware/en/tang/Tang-Nano-4K/Nano-4... https://www.aliexpress.us/item/3256806880637138.html
Cool board! Would any of the "retro" game/home computer firmwares fit in that FPGA? I find comparing capacity hard for stuff like that.
Yes, easily, but unless someone has done it already, 'porting' them to this board would be a lot of work.
Re: SIMD-accelerated computer vision on a $2 microcontroller
#17>For silicon that's cheaper than the average coffee, that's pretty cool.
Maybe it's not the chip that it's too cheap. Maybe it's the coffee that's too expensive.
Re: SIMD-accelerated computer vision on a $2 microcontroller
#18A comparable board is the ESP32-CAM, which is supported by this really practical computer vision project: https://github.com/jomjol/AI-on-the-edge-device?tab=readme-o...
How many fps can that project do?
Re: SIMD-accelerated computer vision on a $2 microcontroller
#19>For silicon that's cheaper than the average coffee, that's pretty cool. Maybe it's not the chip that it's too cheap. Maybe it's the coffee that's too expensive.
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Re: SIMD-accelerated computer vision on a $2 microcontroller
#20> As I've been really interested in computer vision lately, I decided on writing a SIMD-accelerated implementation of the FAST feature detector for the ESP32-S3 [...] > In the end, I was able to improve the throughput of the FAST feature detector by about 220%, from 5.1MP/s to 11.2MP/s in my testing. This is well within the acceptable range of performance for realtime computer vision tasks, enabling the ESP32-S3 to e…
> Is there TPU-like functionality in anything in this price range of chips yet? Kendryte K210 supports 1x1 and 3x3 convolutions on the "TPU". It was pretty well supported in terms of software & documentation but sadly it hasn't become popular. These days, you can easily find cheap RV1103 ("LuckFox"), BL808 ("Ox64/Pine64") and CV1800B/SG20002 ("MilkV") based dev boards, all of which have some sort of basic TPU. Unfort…
> These days, you can easily find cheap RV1103 ("LuckFox"), BL808 ("Ox64/Pine64") and CV1800B/SG20002 ("MilkV") based dev boards, all of which have some sort of basic TPU. Unfortunately, they are designed to be linux boards meaning that all TPU related stuff is extremely abstracted with zero under-the-hood documentation. So it's absolutely unclear whether their TPUs are real or faked with clever code optimizations.
They all have TPU in hardware, my team has been verifying and benchmarking them. Documentation is only available for the high-level C APIs to the libraries that a programmer is expected to use, and even that tends to be extremely lacking.