Re:Shrinking deep learning’s carbon footprint
by westurner on Saturday August 08, 2020 @11:42PM
(#24097082)
The article covers software approaches (more energy-efficient algorithms) and mentions GPUs but not TPUs or ASICs.
Specialized chips (built with dynamic fabrication capacities) are far more energy efficient for specific types of workloads. We see this with mining ASICs, SSL accelerators, and also with Tensor Processing Units (for deep learning).
The externalities of energy production are the ultimate concern. If you're using cheap, clean energy with minimized external costs ("sustainable energy"), the energy-efficiency of the algorithm and the chips is of much less concern.
Could we recognize products, services, and data centers that were produced with and/or run on directly sourced clean energy as "200% Green"; with a logo on the box and/or the footer? 100% offset by PPAs is certainly progress.
Specialized chips (built with dynamic fabrication capacities) are far more energy efficient for specific types of workloads. We see this with mining ASICs, SSL accelerators, and also with Tensor Processing Units (for deep learning).
The externalities of energy production are the ultimate concern. If you're using cheap, clean energy with minimized external costs ("sustainable energy"), the energy-efficiency of the algorithm and the chips is of much less concern.
Could we recognize products, services, and data centers that were produced with and/or run on directly sourced clean energy as "200% Green"; with a logo on the box and/or the footer? 100% offset by PPAs is certainly progress.