William Hwang
Christos Kozyrakis
ABSTRACT
The world's appetite for abundant-data computing, where a massive amount of structured and unstructured data is analyzed, has increased dramatically. The computational demands of these applications, such as deep learning, far exceed the capabilities of today's systems, especially for energy-constrained embedded systems (e.g., mobile systems with limited battery capacity). These demands are unlikely to be met by isolated improvements in transistor or memory technologies, or integrated circuit (IC) architectures alone. Transformative nanosystems, which leverage the unique properties of emerging nanotechnologies to create new IC architectures, are required to deliver unprecedented functionality, performance, and energy efficiency. We show that the projected energy efficiency benefits of domain-specific 3D nanosystems is in the range of 1,000x (quantified using the product of system-level energy consumption and execution time) over today's domain-specific 2D systems with off-chip DRAM. Such a drastic improvement is key to enabling new capabilities such as deep learning in embedded systems.
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