Multi-Objective NAS is a neural architecture search approach that simultaneously optimizes multiple competing objectives — such as accuracy, latency, model size, energy consumption, and memory, producing a Pareto frontier of architectures representing different trade-offs.

How Does Multi-Objective NAS Work?

• Objectives: Accuracy ↑, Latency ↓, Parameters ↓, FLOPs ↓, Energy ↓.
• Pareto Frontier: The set of architectures where no objective can be improved without degrading another.
• Methods: Evolutionary algorithms (NSGA-II), scalarization (weighted sum), or Bayesian optimization.
• Selection: User picks from the Pareto frontier based on deployment constraints.

Why It Matters

• Real-World Trade-offs: No single architecture is best — deployment requires balancing multiple constraints.
• Design Space Exploration: Reveals the fundamental trade-off curves between competing metrics.
• Flexibility: The Pareto set provides multiple deployment options from a single search.

Multi-Objective NAS is architectural diplomacy — finding the set of optimal compromises between accuracy, speed, size, and power consumption.