Trustworthy artificial intelligence increasingly relies on probabilistic computation to achieve robustness, interpretability, security and privacy. In practical systems, such workloads interleave deterministic data access with repeated stochastic sampling across models, data paths and system functions, shifting performance bottlenecks from arithmetic units to memory systems that must deliver both data and randomness. Here we present a unified data-access perspective in which deterministic access is treated as a limiting case of stochastic sampling, enabling both modes to be analyzed within a common framework. This view reveals that increasing stochastic demand reduces effective data-access efficiency and can drive systems into entropy-limited operation. Based on this insight, we define memory-level evaluation criteria, including unified operation, distribution programmability, efficiency, robustness to hardware non-idealities and parallel compatibility. Using these criteria, we analyze limitations of conventional architectures and examine emerging probabilistic compute-in-memory approaches that integrate sampling with memory access, outlining pathways toward scalable hardware for trustworthy AI.

翻译：可信人工智能日益依赖概率计算以实现鲁棒性、可解释性、安全性与隐私保护。在实际系统中，此类工作负载将确定性数据访问与跨模型、数据路径及系统功能的重复随机采样交错进行，使性能瓶颈从算术单元转移至须同时提供数据与随机性的存储系统。本文提出统一数据访问视角，将确定性访问视为随机采样的极限情况，从而在统一框架内分析两种模式。该视角揭示：随机需求的增加会降低有效数据访问效率，并可能驱动系统进入熵受限运行状态。基于此洞见，我们定义了存储级评估准则，包括统一操作、分布可编程性、效率、对硬件非理想性的鲁棒性及并行兼容性。利用这些准则，我们分析了传统架构的局限性，并考察了将采样与存储访问集成的新兴概率存内计算方法，勾勒出面向可信人工智能可扩展硬件的实现路径。