# KALAM: toolKit for Automating high-Level synthesis of Analog computing systeMs

arXiv: https://arxiv.org/abs/2410.22946 · HTML: https://mlsanity.com/paper/2410.22946/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization](https://mlsanity.com/paper/2204.12948/) (2022)
- [Bias-Scalable Near-Memory CMOS Analog Processor for Machine Learning](https://mlsanity.com/paper/2202.05022/) (2022)
- [Process, Bias and Temperature Scalable CMOS Analog Computing Circuits for Machine Learning](https://mlsanity.com/paper/2205.05664/) (2022)

## Abstract (arXiv)

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.