4 ms·

OK, I looked over your paper. Could I actually build this from your paper and your single edge node circuit? I am not sure yet, I did not read it three times ye

OK, I looked over your paper. Could I actually build this from your paper and your single edge node circuit? I am not sure yet, I did not read it three times yet (typically, have to read it multiple times)... but my first impression is that I could not reproduce the papers conclusions. I _feel_ like something is missing. Are you holding off until your provisional patent is approved, or the like? I feel like there is some connecting device/circuitry/something left out... As you mentioned near the end, you do NOT overcome the bias in the AD5220s? You just accept the error floor??

RaisinLoaf69 5y ago
We’re not holding anything back, you should be able to recreate our findings from the paper. More broadly, there are a number of ways to recreate the network using the relatively simple learning rule we provide. In theory, this learning rule will continue to decrease your error forever (in our simulations our error goes down to machine precision). However, with any physical learning network you’re always gonna hit an error floor based on the precision of your components. With our current variable resistors and network size that floor is around 10^-3. With more precise components (like we mention at the end of the paper), that floor will go down significantly.