all AI news
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration. (arXiv:2107.07983v2 [cs.AR] UPDATED)
Jan. 7, 2022, 2:10 a.m. | Zhi-Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina
cs.LG updates on arXiv.org arxiv.org
Exploiting sparsity is a key technique in accelerating quantized
convolutional neural network (CNN) inference on mobile devices. Prior sparse
CNN accelerators largely exploit un-structured sparsity and achieve significant
speedups. Due to the unbounded, largely unpredictable sparsity patterns,
however, exploiting unstructured sparsity requires complicated hardware design
with significant energy and area overhead, which is particularly detrimental to
mobile/IoT inference scenarios where energy and area efficiency are crucial. We
propose to exploit structured sparsity, more specifically, Density Bound Block
(DBB) sparsity for …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Director, Global Procurement Data Analytics
@ Alcon | Fort Worth - Main
Backend Software Engineer, Airbnb for Real Estate
@ Airbnb | United States
Data Scientist
@ Exoticca | Barcelona, Catalonia, Spain - Remote
ESG Data Analytics Summer Associate (Intern)
@ Apex Clean Energy | Charlottesville, VA, United States
Team Lead, Machine Learning
@ Prenuvo | Vancouver, British Columbia, Canada