Real-time scheduling of mixture-of-experts systems with limited resources

Mixture-of-Experts (MoE) systems solve intricate problems by combining results generated independently by multiple computational models (the “experts”). Given an instance of a problem, the responsibility of an expert measures the degree to which the expert’s output contributes to the final solution. Brain Machine Interfaces are examples of applications where an MoE system needs to run periodically and expert responsibilities can vary across execution cycles. When resources are insufficient to run all experts in every cycle, it becomes necessary to execute the most responsible experts within each cycle. The problem of adaptively scheduling experts with dynamic responsibilities can be formulated as a succession of optimization problems. Each of these problems can be solved by a known technique called “task compression” using explicit mappings described in this paper to relate expert responsibilities to task elasticities. A novel heuristic is proposed to enable real-time execut...

• Type: Conference
• Year: 2010
• Where: HYBRID
• Authors: Prapaporn Rattanatamrong, José A. B. Fortes
• Keywords: Expert Responsibilities, Experts, Heuristic, HYBRID 2010, Hybrid Systems