Descripción del proyecto
El aprendizaje automático podría acelerar el diseño de sofisticados ci analógicos
La era de la hiperconectividad ininterrumpida, o «smart-everything», ha supuesto un aumento de la necesidad de tecnologías avanzadas de semiconductores y procesamiento inteligente de datos. A pesar de los avances en el campo, el diseño de circuitos analógicos va a la zaga de su contraparte digital: los circuitos analógicos todavía se fabrican en el laboratorio, lo que conlleva ciclos propensos a errores y costes de desarrollo elevados. En el proyecto AnalogCreate, financiado con fondos europeos, se capitalizará el potencial del aprendizaje automático para acelerar el diseño de circuitos integrados avanzados para aplicaciones prometedoras en el sector de la información y las comunicaciones. Las actividades del proyecto permitirán, por primera vez, el desarrollo autónomo de circuitos analógicos asequibles, desde las especificaciones hasta el diseño completamente verificado, todo ello sin necesidad de participación humana.
Objetivo
Progress in semiconductor technology and in intelligent data processing are converging today, opening the door to countless smart ICT applications through the Cloud and Internet of Everything, to the people’s benefit in years to come. Applications that interact with the physical world (e.g. environmental sensing, healthcare, autonomous vehicles, etc.), also need analog integrated circuits in the cyber-physical or edge layer. But while digital circuits are largely synthesized automatically through software, the analog circuits are mainly still handcrafted in industry with low design productivity. This results in long and error-prone design cycles, and the high development costs jeopardize many potential new ICT applications from ever being realized (e.g. solutions for rare diseases). It becomes even more problematic when moving to advanced technologies below 16 nm CMOS, that come with way more design and layout rules to be dealt with. The showstopper for state-of-the-art analog synthesis tools is that they require design heuristics and constraints to be entered explicitly by designers in order to handle the humongous solution space and to steer the circuit and layout optimizations towards acceptable solutions. The proposed disruptively new approach is to use the self-learning capabilities of advanced machine learning algorithms to self-learn and then exploit the design expertise and constraints from the many available successfully completed designs. Also a true circuit topology synthesis approach will be developed to create a proper (possibly novel) schematic from the target specifications, as well as an innovative formal analog design verification approach based on Quick Error Detection. These innovations will enable for the first time ever to truly autonomously create analog circuits from specifications to fully verified layout without direct input from any designer in the loop, and therefore enable the affordable implementation of many promising ICT applications.
Ámbito científico
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringanalogue electronics
- natural sciencescomputer and information sciencesdata sciencedata processing
Palabras clave
Programa(s)
Régimen de financiación
ERC-ADG - Advanced GrantInstitución de acogida
3000 Leuven
Bélgica