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.

Researchers at the Indian Institute of Science (IISc) have developed a design framework to build next-generation analog computing chipsets that could be faster and require less power than the digital chips found in most electronic devices. Using their novel design framework, the team has built a prototype of an analog chipset called ARYABHAT-1 (Analog Reconfigurable technologY And Bias-scalable Hardware for AI Tasks). This type of chipset can be especially helpful for Artificial Intelligence (AI)-based applications like object or speech recognition--think Alexa or Siri--or those that require massive parallel computing operations at high speeds. Most electronic devices, particularly those that involve computing, use digital chips because the design process is simple and scalable. "But the advantage of analog is huge. You will get orders of magnitude improvement in power and size," explains Chetan Singh Thakur, assistant professor at the Department of Electronic Systems Engineering (DESE), IISc, whose lab is leading the efforts to develop the analog chipset.

Over its lifetime, the average car is responsible for emitting about 126,000 pounds of the greenhouse gas carbon dioxide (CO2). Compare those emissions with the carbon footprint left behind by artificial intelligence (AI) technology. In 2019, training top-of-the-line artificial intelligence was responsible for more than 625,000 pounds of CO2 emissions. AI energy requirements have only gotten bigger since. To reduce AI's energy footprint, Shantanu Chakrabartty, the Clifford W. Murphy Professor at the McKelvey School of Engineering at Washington University in St. Louis, has reported a prototype of a new kind of computer memory.