Before revision:  Thus, overall, our work demonstrates low-energy spiking network processing on an analog neuromorphic substrate and sets several new benchmarks for hardware systems in terms of classification accuracy, processing speed, and efficiency. Importantly, our work emphasizes the value of hardware-in-the-loop training and paves the way toward energy-efficient information processing on non-von-Neumann architectures .
After revision:  Thus, overall, our work demonstrates low-energy spiking network processing on analog neuromorphic substrates and constitutes an important step toward on-chip learning .