Edge computing is an emerging IT architecture that enables the processing of data locally by smartphones, autonomous vehicles, local servers, and other IoT devices instead of sending it to be processed at a centralized large data center. This approach could allow artificial intelligence (AI) models and other computational systems to perform tasks rapidly, while consuming less power.

Despite the potential of this approach, typically local devices have a limited battery capacity and restricted computing capabilities. This means they often need to send data to remote cloud servers via the internet to complete complex calculations. This transmission of information via wireless communication can consume significant amounts of energy, while also slowing down the rates of transmission.

Researchers at Nanjing University recently introduced a new approach that could potentially boost the speed of communication between edge devices and cloud servers, while also reducing energy consumption. Their proposed strategy, introduced in a paper published in Nature Electronics, relies on newly developed communication-aware in-memory wireless neural networks, new computational tools that combine computing, memory, and wireless communication into a single AI-powered system.