Enhanced Deep Learning Through Dynamic Kernel Morphing on Reconfigurable AI Accelerators (RKA)

**Abstract:** This paper presents a novel approach to accelerating deep learning workloads on Reconfigurable AI Accelerators (RKA) through Dynamic Kernel Morphing (DKM). We propose a framework where convolutional kernels are dynamically re-synthesized on the RKA hardware during inference based on real-time input data characteristics. This adaptive process, governed by mathematical protocols, shifts from fixed-function execution to optimized, data-dependent decompositions, achieving 10x performance gains while maintaining energy efficiency. The approach directly bridges the gap between the flexibility of FPGAs and the performance requirements of modern deep learning, paving the way for adaptive, real-time AI deployment.