3 ms·

Probably the most common matrix multiplication is (nx9) x (9xm) (9 = 3x3 cells from a convnet) . If you can optimize the shit out of those you might be in busi

Probably the most common matrix multiplication is (nx9) x (9xm) (9 = 3x3 cells from a convnet) . If you can optimize the shit out of those you might be in business for something interesting. Though to be honest the real slow step in machine learning is training and the slow step in training is the outer product of two matrices.... I don't believe there is an algorithmic way out of that one. For non-ml/non-GF purposes, you might also worry about numerical stability of these matrix multiplications, which is not addressed in this paper.

a1369209993 4y ago
> and the slow step in training is the outer product of two matrices.... I don't believe there is an algorithmic way out of that one. Well, there are several, but the obvious ones tend to require strange or unrealistic assumptions about the hardware. The most obvious such assumption, IMO, being that the hardware is arranged in 3D space in a manner roughly analogous to a human brain, which tends to be at odds with the common practice of mostly-planar photolithography, and with the preference to be able to change the network topology experimentally without building new hardware.