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Low-Precision Linear Algebra For Neural Networks, Frost Bennion Mitchell
Low-Precision Linear Algebra For Neural Networks, Frost Bennion Mitchell
Undergraduate Honors Capstone Projects
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, only relies on OpenMP as a dependency, and is portable to any operating system. LPLANN also includes optimizations to provide …