OK. Why would you consider it a realistic enough prospect to study it or write this post? I know there were people doing analog multiplies with light absorption, but even 8-bit analog data transmission with light uses more energy than an 8-bit multiply with transistors. The physics of optical transistors don’t seem compatible with lower energy than electrons. What hope do you think there is?
I think you are assuming optical transistors and photonic computing are the same thing, but they are not. Optical transistors are a component that could be used for photonic computing, but they are not necessary, and companies may have a better shot at getting photonic computing to work at scale without them.
Optical transistors try to function similarly to electronic transistors but use photons instead of electrons for signal processing. You are correct that optical transistors are not currently not great and it’s an active area of research to get it to work.
However, photonic computing is a broader concept that may or may not involve optical transistors as some of its components. Given the limitations of current optical transistors (as you point out), I understand that companies working on this typically use alternative photonic techniques to make it more feasible and practical for deep learning matrix multiplication.
Optical transistors are just not as technologically mature (and may never be) as photonic components like modulators and waveguides. For example, the paper I linked in the post is titled “Experimentally realized in situ backpropagation for deep learning in photonic neural networks”, they do not use optical transistors. Instead, they use some of the following components: Mach-Zehnder interferometers, thermo-optic phase shifters, Photonic integrated circuits, and Silicon photonic waveguides.
The final setup allows for matrix operations for backpropagation.
I agree, I meant that this is the promise (which has yet to be realized).
OK. Why would you consider it a realistic enough prospect to study it or write this post? I know there were people doing analog multiplies with light absorption, but even 8-bit analog data transmission with light uses more energy than an 8-bit multiply with transistors. The physics of optical transistors don’t seem compatible with lower energy than electrons. What hope do you think there is?
I think you are assuming optical transistors and photonic computing are the same thing, but they are not. Optical transistors are a component that could be used for photonic computing, but they are not necessary, and companies may have a better shot at getting photonic computing to work at scale without them.
Optical transistors try to function similarly to electronic transistors but use photons instead of electrons for signal processing. You are correct that optical transistors are not currently not great and it’s an active area of research to get it to work.
However, photonic computing is a broader concept that may or may not involve optical transistors as some of its components. Given the limitations of current optical transistors (as you point out), I understand that companies working on this typically use alternative photonic techniques to make it more feasible and practical for deep learning matrix multiplication.
Optical transistors are just not as technologically mature (and may never be) as photonic components like modulators and waveguides. For example, the paper I linked in the post is titled “Experimentally realized in situ backpropagation for deep learning in photonic neural networks”, they do not use optical transistors. Instead, they use some of the following components: Mach-Zehnder interferometers, thermo-optic phase shifters, Photonic integrated circuits, and Silicon photonic waveguides.
The final setup allows for matrix operations for backpropagation.