Energy Complexity
Define the minimum expected energy required to solve a task at a specified quality and reliability level.
LowJoule is a research initiative exploring the mathematical, algorithmic, and computational foundations of energy-efficient artificial intelligence.
Maximize useful output quality while explicitly accounting for the energy required to produce it.
Contemporary AI systems typically allocate substantial computation regardless of the intrinsic difficulty of a task. LowJoule studies whether computation can instead be allocated dynamically according to expected marginal utility.
UI/J is a proposed framework for studying the relationship between useful model performance and the physical energy required to achieve it.
Define the minimum expected energy required to solve a task at a specified quality and reliability level.
Measure the expected quality improvement produced by an additional unit of energy.
Select computational actions dynamically instead of allocating the same computation to every task.
If tasks have heterogeneous computational requirements, a sufficiently accurate adaptive policy can reduce expected energy consumption while maintaining a specified quality constraint.
Mathematical foundations, experimental protocols, benchmarks, and technical papers will be published as the LowJoule research program develops.