Deep counter networks for asynchronous event-based processing

Understand

Despite their advantages in terms of computational resources, latency, and power consumption, event-based implementations of neural networks have not been able to achieve the same performance figures as their equivalent state-of-the-art deep network models.

• We propose counter neurons as minimal spiking neuron models which only require addition and comparison operations, thus avoiding costly multiplications.
• We show how inference carried out in deep counter networks converges to the same accuracy levels as are achieved with state-of-the-art conventional networks.
• As their event-based style of computation leads to reduced latency and sparse updates, counter networks are ideally suited for efficient compact and low-power hardware implementation.