An Energy Efficient In-Memory Computing Machine Learning Classifier Scheme

Large-scale machine learning (ML) algorithms require extensive memory interactions. Managing or preventing data movement can significantly increase the speed and efficiency of many ML tasks. Towards this end, we devise an energy efficient in-memory computing kernel for a ML linear classifier and a prototype is designed. Compared with another in-memory computing kernel for ML applications [1], we achieve a power savings of over 6.4 times than a conventional discrete system while improving reliability by 54.67%. We employ a split-data-aware technique to manage process, voltage and temperature variations. We utilize a trimodal architecture with hierarchical tree structure to further decrease power consumption. Our scheme provides a fast, energy efficient, and competitively accurate binary classification kernel.