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.

近期人工智能算法的突破强调了真正释放人工智能潜力所需的新型计算硬件的需求。本文介绍了第一个连续变量热力学计算机，称为随机处理单元 (SPU)，它由 RLC 电路组成，在一个印刷电路板上，有 8 个单元，通过开关电容进行全对全耦合。我们展示了 SPU 在高斯采样、矩阵求逆等方面的应用，以及在神经网络分类的不确定性量化中的适用性，我们预计当该硬件规模扩大时，将对加速各种概率性人工智能应用产生重大影响。

用于人工智能应用的热力学计算系统

Thermodynamic Computing System for AI Applications

We develop a physics-based model for classical computation based on
autonomous quantum thermal machines. These machines consist of few interacting
quantum bits (qubits) connected to several environments at different
temperatures. Heat flows through the machine are here exploited for computing.
The process starts by setting the temperatures of the environments according to
the logical input. The machine evolves, eventually reaching a non-equilibrium
steady state, from which the output of the computation can be determined via
the temperature of an auxilliary finite-size reservoir. Such a machine, which
we term a "thermodynamic neuron", can implement any linearly-separable
function, and we discuss explicitly the cases of NOT, 3-majority and NOR gates.
In turn, we show that a network of thermodynamic neurons can perform any
desired function. We discuss the close connection between our model and
artificial neurons (perceptrons), and argue that our model provides an
alternative physics-based analogue implementation of neural networks, and more
generally a platform for thermodynamic computing.

基于自治量子热机的物理模型可以实现任何线性可分函数，并可作为神经网络的物理模拟实现以及热力学计算的平台。