Unified Memory Architecture for Probabilistic Trustworthy AI: Hardware-Software Co-Design for 10-100x Speedup

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Proposes a unified memory perspective showing that trustworthy AI workloads (robustness, interpretability, security, privacy) share a common computational pattern: interleaved deterministic data access with stochastic sampling. Argues that emerging memory technologies (processing-in-memory, near-memory computing) can accelerate probabilistic computation 10-100x over current GPU-centric approaches. Bridges the gap between trustworthy AI theory and practical hardware realization for production deployment.