by Angela Guess
Ann Steffora Mutschler recently wrote in Semiconductor Engineering, “Cognitive computing. Artificial intelligence. Machine learning. All of these are concepts [that] aim to make human types of problems computable, whether it be a self-driving car, a health care-providing robot, or a walking and talking assistant robot for the home or office. R&D teams around the world are working to create a whole new world of machines more intelligent than humans. Designing systems as complex as the human brain — which is still largely a mystery — is no small task. For example, tomorrow’s bleeding edge cars will be the ultimate in efficient system-level design sophistication given the complexity, integration, interdependencies, safety, convenience and comfort required on so many levels. ‘It fundamentally changes the paradigm and even what we expect of processors to be doing,’ said Chris Rowen, a Cadence fellow and CTO of the IP Group. ‘But far-out models may take us three decades to realize in terms of biological computing, quantum computing or other approaches.’”
She continues, “He noted that the biggest benefit will be on the energy-efficiency side. This is a key aspect to making cognitive systems of the future a reality because all of the extremely sophisticated processing requires energy. Because many of these systems may be untethered, that energy will have to be carefully meted out. In addition to processing efficiency, the software must be efficient. ‘There are lots of things we can think of that would change the way software gets constructed and where the time and energy is spent in your average computer system,’ Rowen said. ‘Just think about what we have done with the last 50 years of computing. It’s not as if we take the old applications and run them eight orders of magnitude faster. What we do is come up with new kinds of applications, which are really new levels of abstraction.’”
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