Background
Deep convolutional neural networks (CNNs) are rapidly becoming the dominant approach to computer vision and a major component of many other pervasive machine learning tasks.
Technology
Developed is a novel CNN hardware accelerator with a new architecture and design methodology. Modified is the order in which the original input data are brought on to the chip. The design approach is a pyramid-shaped multi-layer sliding window, allowing effective on-chip caching during evaluation. Caching in turn reduces the off-chip memory bandwidth requirements.
Advantages
The proposed technology is an improvement in energy efficiency by minimizing data movements and improving performance.
Application
CNN accelerator architectures that focus on the dataflow across convolutional layers.
Inventors
Michael Ferdman, , Computer Science
Peter Milder, , Electrical Engineering
Monaj Alwani, student, Computer Science
Licensing Potential
Development partner - Commercial partner - Licensing
Licensing Status
Available for License. Stony Brook University seeks to develop and commercialize, by an exclusive or non-exclusive license agreement and/or sponsored research, with a company active in the area.
Licensing Contact
Donna Tumminello, Assistant Director, Intellectual Property Partners, donna.tumminello@stonybrook.edu, 6316324163
Patent Status
Patent application submitted - Provisional patent
US Provisional Filed
Tech Id
8826