In this episode of Brains and Machines, Dr. Terry Sejnowski talks to Dr. Sunny Bains of the University College London about how information flows both ways between neuroscience and engineered intelligence, proposes a new way of looking at memory and considers the Hopfield-Hinton Nobel Prize. 

Dr. Jennifer Hasler of Georgia Tech is best known for her work with field programmable analog arrays (FPAAs). In this episode of Brains and Machines, she talks about the importance of, and progress in, analog electronics for AI with Dr. Sunny Bains of the University College London. Discussion follows with Dr .Giulia D’Angelo from the Czech Technical University in Prague and Professor Ralph Etienne-Cummings of Johns Hopkins University.

Dr. Tony Lewis, CTO of BrainChip, and four other key scientists talk to Dr. Sunny Bains of University College London. They discuss their business strategy, their temporal event-based neural network (TENN) and the next iteration of the Akida chip. Discussion follows with Dr. Giulia D’Angelo from the Czech Technical University in Prague and Prof. Ralph Etienne-Cummings of Johns Hopkins University.

In this episode of Brains and Machines, emeritus Prof. Rodney Brooks of Massachusetts Institute of Technology, currently CTO of Robust AI, talks about bottom-up and top-down approaches to robotics and AI with Dr. Sunny Bains of University College London. Discussion follows with Dr. Giulia D’Angelo from the Czech Technical University in Prague and Prof Ralph Etienne-Cummings of Johns Hopkins University.

Many IC design teams struggle with tight deadlines and limited resources. The industry is constantly searching for new ways to improve efficiency without compromising design quality. While they might find tools that run incrementally faster, the real gains come from adjustments to the design flow – including what we call ‘shift-left’ strategies that pull signoff-quality verification into the design implementation stage. Join our host, Eric Singer, for a compelling interview with David Abercrombie, Product Management Director of Artificial Intelligence & Licensing Applications at Siemens EDA.

Dr. Katie Schuman of the University of Tennessee explains the advantages of evolutionary approaches in neural processing to Dr. Sunny Bains of University College London. Discussion follows with Dr. Giulia D’Angelo from the Czech Technical University in Prague and Professor Ralph Etienne-Cummings of Johns Hopkins University.

Dr. Sunny Bains talks to four key figures at Innatera, a spin out from the University of Delft in the Netherlands. They are hoping that their latest spiking neural network chip will become AI of choice for people working on sensor applications. Discussion follows with Dr. Giulia D’Angelo from the Czech Technical University in Prague and Prof. Ralph Etienne-Cummings of Johns Hopkins University.

Renesas, known for its industry-leading MCU and MPU portfolio, has a comprehensive power management portfolio addressing existing and emerging applications. This compelling interview covers how Renesas is addressing power challenges in the automotive and industrial sectors, including solutions for xEV traction inverters, zonal control, and portable power stations. Renesas’ approach emphasizes complete, scalable designs and rapid prototyping, facilitating faster time to market for manufacturers.

UCL’s Dr. Sunny Bains talks parallelism, neural net efficiency and risk taking with Caltech’s Prof. Carver Mead. Now an emeritus professor, Mead has been instrumental in the development of chip design, and was one of the first employees of Noyce and Moore, which later became Intel. He’s also one of the founders of the field of neuromorphic engineering. Discussion follows with Dr Giulia D’Angelo from the Czech Technical University in Prague and Prof. Ralph Etienne-Cummings of Johns Hopkins University.

There is a lot of hype in the industry around AI, but behind the hype there is the reality. That reality is that AI really is impacting virtually every aspect of semiconductor design. However, its not as simple as taking general purpose AI solutions and hoping they work for EDA, the risks are too high and when dealing with parts per billion (or trillion) in acceptable errors, hallucinations are not acceptable. What is needed are Verifiable AI solutions that deliver results that users can trust and that reduce the overall resources needed to complete a task. At Siemens EDA we have been able to leverage Verifiable AI to accelerate virtually every aspect of the design and verification process.