Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for novel computing hardware in order to truly unlock the potential for AI. Physics-based hardware, such as thermodynamic computing, has the potential to provide a fast, low-power means to accelerate AI primitives, especially generative AI and probabilistic AI. In this work, we present the first continuous-variable thermodynamic computer, which we call the stochastic processing unit (SPU). Our SPU is composed of RLC circuits, as unit cells, on a printed circuit board, with 8 unit cells that are all-to-all coupled via switched capacitances. It can be used for either sampling or linear algebra primitives, and we demonstrate Gaussian sampling and matrix inversion on our hardware. The latter represents the first thermodynamic linear algebra experiment. We also illustrate the applicability of the SPU to uncertainty quantification for neural network classification. We envision that this hardware, when scaled up in size, will have significant impact on accelerating various probabilistic AI applications.

翻译：近年来人工智能（AI）算法的突破性进展凸显了对新型计算硬件的迫切需求，以真正释放AI潜力。基于物理机制的热力学计算硬件，有望以低功耗、高速的方式加速AI基元操作，尤其是生成式AI和概率性AI。本文展示了首个连续变量热力学计算机——我们称之为随机处理单元（SPU）。该SPU以印刷电路板上的RLC电路为基本单元，包含8个通过开关电容实现全连接耦合的单元。该装置既可用于采样操作，也可用于线性代数基元计算，我们已在硬件上实现了高斯采样和矩阵求逆。后者代表了首个热力学线性代数实验。我们还展示了SPU在神经网络分类不确定性量化中的应用。我们预计，当该硬件扩展至更大规模时，将对加速各类概率性AI应用产生重要影响。