HfO2-Based Synaptic Spiking Neural Network Evaluation to Optimize Design and Testing Cost

Machine learning based on memristive dot product engine (DPE) suffers from some practical limitations of memristive synapse which requires constraining the resources such as the number of memristive states and current sensing capability at the classification layer. Constraining those resources saves design time, complexity, and resources but impacts the performance of the system. This paper assesses the performance of DPE-based machine learning across different numbers of memristive states and varying the current sensing resolution at the classification layer. The study found that, for small applications, 8 memristive states are sufficient, with minimal impact observed from increasing the number of states with lower current distinguishability. The analysis also finds that the decreasing current sensing resolution negatively impacts the stability of the system by increasing the random behavior.