Although computers are overwhelmingly digital today, there’s a good point to be made that analog computers are the more efficient approach for specific applications. The authors behind a recent paper in Nature are arguing that inference – essential for LLMs – can be done significantly more efficiently using an analog optical computer (AOC).
As the authors describe it, the function of this AOC is to perform a fixed-point search using only optical and analog electronic components. The optics handle the matrix-vector multiplications, while the analog components handle the non-linear operations, subtractions and annealing. This is performed in 20 ns cycles until noise has been reduced to an acceptable level, considering the analog nature of the computer. A big advantage here is that no analog-digital conversions are required as with other (digital) hybrid systems.
So far a small-scale AOC has been constructed for tasks like image classification and non-linear regression tasks, with the authors claiming the AOC being over a hundred times more efficient than current GPU-derived vector processors.
