ML-Driven Memory Management Unit (MMU) in FPGA Architectures

FPGAs are becoming more popular for general-purpose computing, and AI/ML acceleration, but memory management on these reconfigurable hardware elements is still a challenge compared to fixed-logic architectures. We present a new, Machine Learning-driven Memory Management Unit (MMU) architecture for FPGAs, which employs intelligent algorithms (e.g., reinforcement learning and LSTM neural networks) to optimize memory response. In this work, we describe an ML-augmented MMU's architectural design and algorithmic framework, including virtual memory support, adaptive caching/prefetching, and dynamic allocation. Further showcasing latency, throughput, energy efficiency, and memory bandwidth benefits. We also show how we improve security mechanisms, relying on cache timing side-channels and speculative execution vulnerabilities, for cryptographic and ML algorithms. The design is adaptable across various applications (AI inference, high-performance computing, general workloads) and FPGA platforms. Finally, we describe the novelty when applied to a patent context with broad claims on machine learning applied to hardware memory management and security integration. This work is derived from the Provisional Patent Application #63/775,213, entitled "ML-Driven Memory Management Unit (MMU) in FPGA Architectures," filed on Mar 20, 2025, by Raj Sandip Parikh, with the United States Patent and Trademark Office (USPTO).