A small bone of contention: it’s not about Moore’s law per se, which has been ‘dead’ since about 2013, coincidentally when the deep learning revival started. It’s matrix multiplication ASIC development that is driving the progress.
GPUs already existed when the idea to use them to make the feasible size of neural nets larger came about. For a long time the drive for the increase of GPU compute power was still gaming/commercial graphics houses. It’s really only in the last 1-2 years that we’ve seen highly specialised GPUs with features like tensor cores (or indeed google’s TPUs).
Also, calling neural nets ‘brute force’ because they use a lot of computing power to train a model is slightly reductionist - a true brute force approach to image recognition, ie enumerating all possible combinations of, say, 200x200x256x3 pixels, would be completely absurd and probably exceed the computing power currently available on earth.
GPUs already existed when the idea to use them to make the feasible size of neural nets larger came about. For a long time the drive for the increase of GPU compute power was still gaming/commercial graphics houses. It’s really only in the last 1-2 years that we’ve seen highly specialised GPUs with features like tensor cores (or indeed google’s TPUs).
Also, calling neural nets ‘brute force’ because they use a lot of computing power to train a model is slightly reductionist - a true brute force approach to image recognition, ie enumerating all possible combinations of, say, 200x200x256x3 pixels, would be completely absurd and probably exceed the computing power currently available on earth.