Understanding sources of inefficiency in general-purpose chips

By Rehan Hameed, Wajahat Qadeer, Megan Wachs, Omid Azizi, A. Solomatnikov, Benjamin C. Lee, Stephen Richardson, Christoforos E. Kozyrakis, Mark Horowitz. 2010

145 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Hardware and Architecture, ML Systems & Hardware, Signal Processing

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Citations in the corpus

Chasing Carbon: The Elusive Environmental Footprint of Computing (2020, background): “For salient applications, such as AI [4]–[9], molecular dynamics [10], video encoding [11], and cryptography [12], systems now comprise specialized hardware accelerators that provide orders-of-magnitude higher performance and energy efficiency.”
Data Center Energy Consumption Modeling: A Survey (2016, background): “Power breakdown of (a) single-core [142] and (b) multicore processors [143].”
ShiDianNao: Shifting vision processing closer to the sensor (2015, unknown): “We present a full design down to the layout at 65 nm, with a modest footprint of 4.86 mm2 and consuming only 320 mW, but still about 30× faster than high-end GPUs.”
Neural Acceleration for General-Purpose Approximate Programs (2012, background): “Recent work has quantified three orders of magnitude of difference in efficiency between general-purpose processors and ASICs [21, 36].”
DaDianNao: A Machine-Learning Supercomputer (2014, background): “Our community is well aware of the trend towards heterogeneous computing where architecture specialization is seen as a promising path to achieve high performance at low energy [21], provided we can ﬁnd ways to reconcile architecture specialization and ﬂexibility.”