Document Type
Poster
Journal/Book Title/Conference
College of Engineering Senior Design Projects
Publisher
Utah State University
Location
Logan, UT
Publication Date
2019
Abstract
Neural networks have been widely responsible for recent advances in machine learning, powering technologies such as digital assistants and AR photography. LPLANN (Low-Precision Linear Algebra for Neural Networks) is a cross-platform library written in C++ used for implementing neural networks. The software allows users to set specific levels of precision for calculations. Low-precision calculations use advanced parallelization techniques (SIMD, SWAR) to run neural networks at faster rates than full-precision calculations. This library is lightweight enough to run on embedded systems, but also includes optimizations that would provide drastic speedups on a workstation. LPLANN also serves as a testbed for novel low-precision neural network architectures.
Recommended Citation
Mitchell, Frost, "Low-Precision Linear Algebra for Neural Networks" (2019). Electrical and Computer Engineering. Paper 130.
https://digitalcommons.usu.edu/eceengr_srdesign/130