Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication

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This paper presents a new technique for training networks for low-precision communication.

• Targeting minimal communication between nodes not only enables the use of emerging spiking neuromorphic platforms, but may additionally streamline processing conventionally.
• Low-power and embedded neuromorphic processors potentially offer dramatic performance-per-Watt improvements over traditional von Neumann processors, however programming these brain-inspired platforms generally requires platform-specific expertise which limits their applicability.
• To date, the majority of artificial neural networks have not operated using discrete spike-like communication.