2026, Vol. 11, Issue 1, Part B

This paper introduces SYNAPSE-SCALE, an adaptive and intelligent artificial intelligence framework designed to optimize model selection, placement, and continual learning in distributed edge-cloud environments. The system integrates an elastic super-network, a drift-aware constrained contextual bandit router, and lightweight continual learning adapters. Our experimental evaluation compares SYNAPSE-SCALE against cloud-only, edge-only, static elastic, and bandit-based methods under identical non-stationary conditions. Results demonstrate that SYNAPSE-SCALE achieves near-cloud accuracy at significantly lower latency and cost, maintaining over 98% SLA compliance while adapting four times faster to drift. These results establish SYNAPSE-SCALE as a practical, scalable solution for intelligent AI deployment.