Neuromorphic Computing and its Role in Making of a Fully Autonomous Remote Site in Oil and Gas Industry

The advancement in Artificial Intelligence and Deep Neural Networks is leading towards mimicking the human brain’s nervous system and its functioning in day-to-day life. This technical paper presents the idea of a fully autonomous remote site in Oil and Gas industry by utilizing neuromorphic computing and its integration with IIoT sensors. Neuromorphic Computing works on event-based spikes and threshold-based computations and provides a very high efficiency. Neuromorphic computing comes under level three Artificial Intelligence, where the devices and instruments have artificial brains to run themselves autonomously, can repair or give alarms proactively before any actual failure happens, require very minimal power, provide real-time data with ultra-low latency and highest efficiency. The exponential growth of connected devices in IoT environment, Big Data, large bandwidth requirements for data transmission from remote sites to plant and central hubs and further to cloud locations are basic challenges in data transmission networks. The high bandwidth consumption, increase of latency and low throughput are major role players to think beyond Non von Neumann architecture. Neuromorphic computing with edge computing is promising to all the above challenges and provides lots of benefits, such as sending only the required data to important locations. Instruments don’t need to work continuously, but only when it requires, this approach will save bandwidth and power consumption. The autonomous IIoT sensor is no longer a myth, but by utilizing neuromorphic computing with Edge Computing, we can turn fiction into reality.