Designing Energy-Efficient Decision Tree Memristor Crossbar Circuits using Binary Classification Graphs

We propose a method to design in-memory, energy-efficient, and compact memristor crossbar circuits for implementing decision trees using flow-based computing. We develop a new tool called binary classification graph, which is equivalent to decision trees in accuracy but uses bit values of input features to make decisions instead of thresholds. Our proposed design is resilient to manufacturing errors and can scale to large crossbar sizes due to the utilization of sneak paths in computations. Our design uses zero transistor and one memristor (0T1R) crossbars with only two resistance states of high and low, which makes it resilient to resistance drift and radiation degradation. We test the performance of our designs on multiple standard machine learning datasets and show that our method utilizes circuits of size 5.23 × 10−3 mm2 and uses 20.5 pJ per decision, and outperforms state-of-the-art decision tree acceleration algorithms on these metrics.