Approximation

Application of Approximate Matrix Multiplication to Neural Networks and Distributed SLAM

We apply approximate matrix multiplication to artificial Neural Networks for image classification and to the robotics problem of Distributed Simultaneous Localization and Mapping increasing the speed of both applications by 15-20% while maintaining a 97% classification accuracy for NNs running on the MNIST dataset and keeping the average robot position error under 1 meter (vs 0.32 meters for the exact solution). However, both applications experience variance in their results.