随机可微编程框架
A Framework for Stochastic Differentiable Programming
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中文总结 AI 辅助
该研究提出了参数化随机电路（PSCs）及开源JAX框架torx，其与概率硬件原生操作对齐，可直接设计随机算法，经图上随机游走等应用及X0芯片硬件实验验证有效。
中文摘要 AI 辅助
我们引入了参数化随机电路（Parametrized Stochastic Circuits, PSCs），这是一种基于门的中间表示，用于可编程随机动力学，其中带有可调参数的类型化局部随机核通过显式的二元、分类和连续导线组合而成；同时还引入了torx，这是一个开源的JAX框架，用于构建、执行和区分这些电路。PSCs的数据类型和随机核被选择为与新兴概率硬件所暴露的原生操作紧密对齐，通过这种方式，随机算法可以直接根据硬件原生执行的操作进行设计，从而避免了因引入大量分解、通信或控制开销的映射而损失该层面产生的能量优势。我们在多种示例应用中演示了该框架，如图上随机游走、离散扩散、随机图网络、跳跃扩散和伊辛采样。我们还报告了一项硬件实验，其中由XTR-0桌面平台托管的X0亚阈值CMOS测试芯片上的概率比特为Metropolis-Hastings和重要性采样估计器提供物理随机性，产生的估计值与软件伪随机基线一致。
英文摘要
We introduce Parametrized Stochastic Circuits (PSCs), a gate-based intermediate representation for programmable stochastic dynamics in which typed local stochastic kernels with tunable parameters compose over explicit binary, categorical, and continuous wires, and \texttt{torx}, an open-source JAX framework for constructing, executing, and differentiating them. PSCs' data types and stochastic kernels are chosen to align closely with the native operations exposed by emerging probabilistic hardware. In this way, stochastic algorithms can be designed directly in terms of the operations the hardware executes natively, so that the energy advantage arising at this level is not lost on mappings that introduce substantial decomposition, communication, or control overhead. We demonstrate the framework on a variety of example applications such as random walks on graphs, discrete diffusion, stochastic graph networks, jump diffusion and Ising sampling. We also report a hardware experiment in which probabilistic bits on the X0 subthreshold CMOS test chip, hosted by the XTR-0 desktop platform, provide physical randomness for Metropolis-Hastings and importance-sampling estimators, yielding estimates consistent with a software pseudorandom baseline.
