Diverse computing paradigms have emerged to meet the growing needs for intelligent energy-efficient systems. The Margin Propagation (MP) framework, being one such initiative in the analog computing domain, stands out due to its scalability across biasing conditions, temperatures, and diminishing process technology nodes. However, the lack of digital-like automation tools for designing analog systems (including that of MP analog) hinders their adoption for designing large systems. The inherent scalability and modularity of MP systems present a unique opportunity in this regard. This paper introduces KALAM (toolKit for Automating high-Level synthesis of Analog computing systeMs), which leverages factor graphs as the foundational paradigm for synthesizing MP-based analog computing systems. Factor graphs are the basis of various signal processing tasks and, when coupled with MP, can be used to design scalable and energy-efficient analog signal processors. Using Python scripting language, the KALAM automation flow translates an input factor graph to its equivalent SPICE-compatible circuit netlist that can be used to validate the intended functionality. KALAM also allows the integration of design optimization strategies such as precision tuning, variable elimination, and mathematical simplification. We demonstrate KALAM's versatility for tasks such as Bayesian inference, Low-Density Parity Check (LDPC) decoding, and Artificial Neural Networks (ANN). Simulation results of the netlists align closely with software implementations, affirming the efficacy of our proposed automation tool.

翻译：为满足对智能高效能系统日益增长的需求，多种计算范式应运而生。边际传播（MP）框架作为模拟计算领域的一项代表性进展，因其在偏置条件、温度及不断缩小的工艺技术节点间的可扩展性而脱颖而出。然而，由于缺乏用于设计模拟系统（包括MP模拟系统）的类数字自动化工具，阻碍了其在大型系统设计中的广泛应用。MP系统固有的可扩展性与模块化特性为此提供了独特机遇。本文介绍KALAM（模拟计算系统高层次综合自动化工具包），该工具利用因子图作为综合基于MP的模拟计算系统的基础范式。因子图是多种信号处理任务的基础，与MP结合后可设计出可扩展且高能效的模拟信号处理器。KALAM自动化流程采用Python脚本语言，将输入的因子图转换为等效的SPICE兼容电路网表，可用于验证预期功能。该工具还支持集成设计优化策略，如精度调谐、变量消除和数学简化。我们通过贝叶斯推理、低密度奇偶校验（LDPC）解码和人工神经网络（ANN）等任务展示了KALAM的多功能性。网表仿真结果与软件实现高度吻合，证实了所提自动化工具的有效性。