首页|Study Results from University of California San Diego (UCSD) Broaden Understandi ng of Machine Learning (An Open-Source ML-Based Full-Stack Optimization Framewor k for Machine Learning Accelerators)
Study Results from University of California San Diego (UCSD) Broaden Understandi ng of Machine Learning (An Open-Source ML-Based Full-Stack Optimization Framewor k for Machine Learning Accelerators)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting originating from La Jolla, United States, by NewsRx correspondents, research stated, "Parameterizable machine lear ning (ML) accelerators are the product of recent breakthroughs in ML."The news correspondents obtained a quote from the research from University of Ca lifornia San Diego (UCSD): "To fully enable their design space exploration (DSE) , we propose a physical-design-driven, learning-based prediction framework for h ardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It adop ts a unified approach that combines power, performance, and area (PPA) analysis with frontend performance simulation, thereby achieving a realistic estimation o f both backend PPA and system metrics such as runtime and energy."
University of California San Diego (UCSD )La JollaUnited StatesNorth and Central AmericaCyborgsEmerging Technol ogiesMachine Learning
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