The matrix-multiplication is partially done in an "analog" way, multiplication with constant coefficients can be done using programmed optical attenuation elements, and the summing stage can be done by superposition of multiple optical signals.
This works well if you can do with a low dynamic range (low precision math) and with seldomly-changing coefficients (i.e. multiplying a changing vector/matrix with a constant matrix). On the other hand you save orders of magnitude of energy per operation. Maybe for neural networks this is a favourable trade-off. The paper at the link above talks about a 4-bit precision matrix multiplication.
https://aip.scitation.org/doi/10.1063/5.0001942
which seems to describe a similar technology.
The matrix-multiplication is partially done in an "analog" way, multiplication with constant coefficients can be done using programmed optical attenuation elements, and the summing stage can be done by superposition of multiple optical signals.
This works well if you can do with a low dynamic range (low precision math) and with seldomly-changing coefficients (i.e. multiplying a changing vector/matrix with a constant matrix). On the other hand you save orders of magnitude of energy per operation. Maybe for neural networks this is a favourable trade-off. The paper at the link above talks about a 4-bit precision matrix multiplication.