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Welcome to the DNN tutorial website! A summary of all DNN related papers from our group can be found here. DNN related websites and resources can be found here. To find out more about the Eyeriss project, please go here. To find out more about other on-going research in the Energy-Efficient Multimedia Systems (EEMS) group at MIT, please go here. Follow @eems_mit or subscribe to our mailing list fo
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without
Bokeh documentation# Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming datasets. With Bokeh, you can create JavaScript-powered visualizations without writing any JavaScript yourself. Finding the right documentation resources# Bokeh’s documentation consists
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