As you can see in my Figure in this post (https://www.lesswrong.com/posts/75dnjiD8kv2khe9eQ/measuring-hardware-overhang), Leela (Neural Network based chess engine) has a very similar log-linear ELO-FLOPs scaling as traditional algorithms. At least in this case, Neutral Networks scale slightly better for more compute, and worse for less compute. It would be interesting to determine if the bad scaling to old machines is a universal feature of NNs. Perhaps it is: NNs require a certain amount of memory, etc., which gives stronger constraints. The conclusion would be that the hardware overhang is reduced: Older hardware is less suitable for NNs.
As you can see in my Figure in this post (https://www.lesswrong.com/posts/75dnjiD8kv2khe9eQ/measuring-hardware-overhang), Leela (Neural Network based chess engine) has a very similar log-linear ELO-FLOPs scaling as traditional algorithms. At least in this case, Neutral Networks scale slightly better for more compute, and worse for less compute. It would be interesting to determine if the bad scaling to old machines is a universal feature of NNs. Perhaps it is: NNs require a certain amount of memory, etc., which gives stronger constraints. The conclusion would be that the hardware overhang is reduced: Older hardware is less suitable for NNs.