We seek to improve the state of the art in the areas where learning has already proven to perform better than traditional heuristics, as well as expand to new areas throughout the system stack such as hardware/circuit design and operating/runtime systems.īy forming a community of academic and industrial researchers who are excited about this area, we seek to build towards intelligent, self optimizing systems and answer questions such as: How do we generate and share high quality datasets that span the layers of the system stack? Which learned representations best represent code performance and runtime? Which simulators and simulation methodologies provide a tractable proving ground techniques like reinforcement learning? The focus of this workshop is to expand upon this recent work and build a community focused on using machine learning in computer systems problems. Very recent work has outlined a broad scope where deep learning vastly outperforms traditional heuristics including topics such as: scheduling 1,2, data structure design 3, microarchitecture 4, compilers 5, and control of warehouse scale computing systems 6.
#Jeff dean google keynote software#
However, using machine learning to optimize and accelerate software and hardware systems is a lightly explored but promising field, with broad implications for computing as a whole.
Designing specialized hardware for deep learning is a topic that has received significant research attention, both in industrial and academic settings, leading to exponential increases in compute capability in GPUs and accelerators.
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