提出统一内存视角，让随机与确定性访问共用一套机制
A Unified Memory Perspective for Probabilistic Trustworthy AI
- 将确定性访问视为随机采样的特例，统一建模数据访问
- 发现随机需求增加会降低有效访问效率，可能进入熵限制状态
- 为可信AI硬件设计提供新评估标准，适合系统架构研究者
可信人工智能越来越多依赖概率计算以实现鲁棒性、可解释性、安全性和隐私保护。在实际系统中，这类工作负载在模型、数据路径和系统功能间交替进行确定性数据访问与重复的随机采样，使性能瓶颈从算术单元转移到必须同时提供数据与随机性的内存系统。本文提出一种统一的数据访问视角，将确定性访问视为随机采样的极限情况，使两种模式可在同一框架下分析。该视角揭示，随着随机需求上升，有效数据访问效率下降，可能导致系统进入熵限制运行状态。基于此，我们定义了内存级评估标准，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。利用这些标准，我们分析了传统架构的局限性，并考察了将采样与内存访问融合的新兴存内计算方案，勾勒出面向可信AI的可扩展硬件路径。
原文摘要 · Abstract (English)
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.
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