Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

Understand

Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge.

• We have previously demonstrated that traditional CNNs can be converted into deep spiking neural networks (SNNs), which exhibit similar accuracy while reducing both latency and computational load as a consequence of their data-driven, event-based style of computing.
• Here we provide a novel theory that explains why this conversion is successful, and derive from it several new tools to convert a larger and more powerful class of deep networks into SNNs.
• We identify the main sources of approximation errors in previous conversion methods, and propose simple mechanisms to fix these issues.