AUTHOR=Hoskins Brian D. , Daniels Matthew W. , Huang Siyuan , Madhavan Advait , Adam Gina C. , Zhitenev Nikolai , McClelland Jabez J. , Stiles Mark D. TITLE=Streaming Batch Eigenupdates for Hardware Neural Networks JOURNAL=Frontiers in Neuroscience VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00793 DOI=10.3389/fnins.2019.00793 ISSN=1662-453X ABSTRACT=

Neural networks based on nanodevices, such as metal oxide memristors, phase change memories, and flash memory cells, have generated considerable interest for their increased energy efficiency and density in comparison to graphics processing units (GPUs) and central processing units (CPUs). Though immense acceleration of the training process can be achieved by leveraging the fact that the time complexity of training does not scale with the network size, it is limited by the space complexity of stochastic gradient descent, which grows quadratically. The main objective of this work is to reduce this space complexity by using low-rank approximations of stochastic gradient descent. This low spatial complexity combined with streaming methods allows for significant reductions in memory and compute overhead, opening the door for improvements in area, time and energy efficiency of training. We refer to this algorithm and architecture to implement it as the streaming batch eigenupdate (SBE) approach.