On a recent Monday morning, Vivienne Sze, an associate professor of electrical engineering and computer science at MIT, spoke with enthusiasm about network architecture design. Her students nodded slowly, as if on the verge of comprehension. When the material clicked, the nods grew in speed and confidence. “Everything crystal clear?” she asked with a brief pause and a return nod before diving back in. This new course, 6.S082/6.888 (Hardware Architecture for Deep Learning), is modest in size — capped at 25 for now — compared to the bursting lecture halls characteristic of other MIT classes focused on machine learning and artificial intelligence. But this course is a little different. With a long list of prerequisites and a heavy base of assumed knowledge, students are jumping into deep water quickly. They blaze through algorithmic design in a few weeks, cover the terrain of computer hardware design in a similar period, then get down to the real work: how to think about making these two fields work together. Read more here…

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