Summary
A penny saved is a penny earned. Waste not, want not. When we apply these proverbs to machine learning, we start to ask ourselves – is our math too hot?
Today’s machine learning (ML) models only prioritize performance, often overlooking other important characteristics like electricity power consumption. Like ordering from a menu with no prices, you cannot determine the electric cost of an ML model before purchasing it – and you might not like the bill. More formally, we lack a principled way to predict a model’s power consumption, which leads to planning without knowing all the costs.
For warfighters operating in power-constrained environments, these oversights can challenge their ability to adapt their AI tools on the edge (i.e., near where they use the tool and collect data), which can compromise their ability to achieve their missions.
For example, the Department of Defense would benefit from new solutions for power-constrained edge computing, where the total power for an unmanned aerial system (UAS) mission must be shared across all vital functions. In the UAS application, a slight predicted degradation in performance may be acceptable in exchange for increased range to complete a mission.
Mapping Machine Learning to Physics (ML2P) aims to increase the military’s ability to adapt ML on the battlefield by providing energy-aware ML and enabling the strategic use of limited power resources.
The program will map ML efficiency to physics by using precise (forensic) granular measurements in joules (J), which ensures metrics are directly comparable across hardware architectures from analog to photonic computing. ML2P will construct energy-aware ML optimized for power (J) and performance for a given task (e.g., clustering, classification) and candidate hardware for the life cycle of the model by exploring two prerequisite areas:
- Develop objective functions that are optimal, feasible, and provide the desired trade-off for power and performance for a diversity of objective functions and a given application (e.g., data, task, and hardware), enabling energy-aware ML construction.
- Discover the power-performance interactions between local optimizations via capturing the energy semantics of ML and enabling optimization of the non-convex, energy-aware ML problem. Simply put, the program will first document how local optimizations interact, then optimize for a given point in the model’s life cycle to illuminate the optimal energy-aware ML solution.
We’ll ask ML2P performers to look at a wide range of modeling, including generative, classification, clustering, etc., to maximize their utility in the future.
Additionally, ML2P will enable academia to continue open-source research and the defense industrial base to transition ML2P into specific edge applications.