Recently, Julia Computing has been awarded funding by the US Defense Advanced Research Projects Agency (DARPA). The funding was awarded to accelerate the simulation of analog and mixed-signal circuit models using state of the art machine learning and artificial intelligence techniques.
Last year, the Ditto program is launched by DARPA to explore the novel third-wave AI solutions through the lens of microelectronic system simulation. DARPA stated that the effort seeks to develop an automated software framework that can take in a microelectronic system design, train effective ML surrogate models of sub-system components, and simulate these designs 1000x faster while maintaining the acceptable levels of accuracy.
Replying to this comment, Keno Fischer, who is the project PI and CTO at Julia Computing said, “Julia’s performance and differentiable programming capabilities give us a unique advantage in creating novel tools for modelling and simulation.”
He added, “Using newly developed surrogate architectures, such as our Continuous Time Echo State Network (CTESN) architecture, we have already been able to demonstrate acceleration in excess of 100x by employing these techniques in multi-physics simulations and are excited to bring this technology to the electronics simulation space.”
The company is partnering with Boston-based quantum computing startup QuEra Computing to demonstrate these novel capabilities for simulations of the control electronics of QuEra’s neutral atom quantum computers.
Julia is one of the high-performance languages of choice for data science, artificial intelligence, and modelling and simulation applications. According to sources, the sophisticated designs of the Boston-based quantum computing startup stretch the boundaries of traditional simulation tooling, making a significant acceleration in simulation performance all the more crucial. Julia Computing intends to make these capabilities available to the larger industry in the near future.
The programming language company mentioned that the companies who are facing challenges in analog/mixed-signal modelling and simulation problems are encouraged to contact Julia Computing.
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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.