Spiking Networks for Improved Cognitive Abilities of Edge Computing Devices
This is an incremental approach for edge computing devices to handle personal data more effectively.
The paper proposes using spiking neural networks to enable large-scale analytical algorithms to be trained directly on edge devices, addressing the need for processing personal data locally with low latency and energy efficiency.
This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices. Such approach is a response to the arising need of processing data generated by natural person (a human being), also known as personal data. Spiking Neural networks are the core method behind it: suitable for a low latency energy-constrained hardware, enabling local training or re-training, while not taking advantage of scalability available in the Cloud.