On: Implementing Neural Networks on a "10-cent" RISC-V...

[_Microft]

How would one get these 16x16 images generated in a way that does not need a lot more compute power than the inference itself? Maybe by using a sensor from an optical mouse which seems to have a similar resolution? [0] According to a quick web-search, the CH32V003 seems to support SPI and I²C out of the box [1] which the mentioned sensor supports?

What would one do with such a system?

[0] https://pickandplace.wordpress.com/2012/05/16/2d-positioning...

[1] https://www.wch-ic.com/products/CH32V003.html

[jononor → _Microft]

IO does tend to take considerable resources/power. In fact it is one of the reasons it is desirable to run ML as close to the sensor as possible. It allows to extract and transmit onwards just the information of interest (usually very low bitrate) instead of raw sensor data. Especially important on wireless and battery.

One area where very low resolution images are used is in 3d and IR sensing. For example a 8x8 depth image from a time of flight sensor like ST VL53L5CX. Could be mounted say household and detect for example human vs pet vs static object. Though the sensor is the expensive part, so one would probably afford a larger microcontroller :D

[cpldcpu → _Microft]

Indeed using a mouse sensor for data input would be quite interesting. Mayber another option would just be a row of phototransistors.

[imtringued → cpldcpu]

"ESP32 CAM" gets me a couple of hits for a camera excluding ESP32 for under $1.50 at 500 minimum quantity.

[mianos → imtringued]

Specially considering the esp32 has multiply for both integer and floatinf point.

[imtringued → _Microft]

7 seconds is an eternity, even for micro controllers.

[robxorb → imtringued]

Unless I'm misreading your comment, you may have misread the article.

Inference for this RISC-V implementation takes 13.7ms. 7 seconds was cited from an Arduino version as a reference.