In this paper, we demonstrate how the physics of entropy production, when combined with symmetry constraints, can be used for implementing high-performance and energy-efficient analog computing systems. At the core of the proposed framework is a generalized maximum-entropy principle that can describe the evolution of a mesoscopic physical system formed by an interconnected ensemble of analog elements, including devices that can be readily fabricated on standard integrated circuit technology. We show that the maximum-entropy state of this ensemble corresponds to a margin-propagation (MP) distribution and can be used for computing correlations and inner products as the ensemble's macroscopic properties. Furthermore, the limits of computational throughput and energy efficiency can be pushed by extending the framework to non-equilibrium or transient operating conditions, which we demonstrate using a proof-of-concept radio-frequency (RF) correlator integrated circuit fabricated in a 22 nm SOI CMOS process. The measured results show a compute efficiency greater than 2 Peta ($10^{15}$) Bit Operations per second per Watt (PetaOPS/W) at 8-bit precision and greater than 0.8 Exa ($10^{18}$) Bit Operations per second per Watt (ExaOPS/W) at 3-bit precision for RF data sampled at rates greater than 4 GS/s. Using the fabricated prototypes, we also showcase several real-world RF applications at the edge, including spectrum sensing, and code-domain communications.

翻译：本文论证了熵产生物理学与对称性约束相结合，如何用于实现高性能、高能效的模拟计算系统。该框架的核心是一个广义最大熵原理，能够描述由互连模拟元件集合构成的介观物理系统的演化过程，这些元件包括可在标准集成电路工艺中直接制造的器件。我们证明该集合的最大熵态对应于边缘传播分布，并可通过系统的宏观特性用于计算相关性和内积。此外，通过将框架扩展至非平衡或瞬态工作条件，可突破计算吞吐量和能效的极限。我们使用基于22纳米SOI CMOS工艺制造的概念验证射频相关器集成电路对此进行了演示。测量结果显示：在8位精度下，计算效率超过2 Peta（10^15）比特操作每秒每瓦；在3位精度下，对于采样率大于4 GS/s的射频数据，计算效率超过0.8 Exa（10^18）比特操作每秒每瓦。利用制造的样机，我们还展示了多种边缘射频实际应用，包括频谱感知和码域通信。