KALAM自动化工具让模拟计算系统设计如数字电路般高效。
KALAM: toolKit for Automating high-Level synthesis of Analog computing systeMs
- 用因子图建模模拟计算，实现自动电路生成
- 支持贝叶斯推断、LDPC解码等任务，仿真结果与软件一致
- 适合需要低功耗模拟处理器的芯片设计师
为满足智能节能系统日益增长的需求，多种计算范式应运而生。其中，基于边际传播（MP）的模拟计算框架因其在偏置条件、温度及工艺节点缩小下的可扩展性而备受关注。然而，缺乏类似数字电路的自动化设计工具，限制了其在大型系统中的应用。MP系统的固有可扩展性与模块化特性为此提供了契机。本文提出KALAM（toolKit for Automating high-Level synthesis of Analog computing systeMs），以因子图为基础，实现基于MP的模拟计算系统高层综合。因子图广泛应用于各类信号处理任务，结合MP可构建可扩展、低功耗的模拟信号处理器。通过Python脚本，KALAM将输入的因子图转换为SPICE兼容的电路网表，用于功能验证，并支持精度调优、变量消除与数学简化等优化策略。我们展示了KALAM在贝叶斯推断、低密度奇偶校验（LDPC）解码和人工神经网络（ANN）中的适用性。网表仿真结果与软件实现高度一致，验证了该自动化工具的有效性。
原文摘要 · Abstract (English)
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.
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