The Internet-of-Things (IoT) relies on a large number of sensors, actuators, processing units and networking units capable of giving services that could not be implemented otherwise.
An important requirement is the dynamic extendability of the network structure (adding nodes) and of the applications offered by the network (adding services).
The particular deployment of many IoT implementations requires energy efficiency maximization, while guaranteeing the required quality of service and the dynamic extendability. In fact, the IoT relies on the possibility of demanding data processing to centralized servers of even data-centers (cloud computing); yet, in order to maximize energy efficiency, local processing close to the sensors is pursued in present implementations (edge-computing), thus requiring ad-hoc computing resources in the edge-nodes.
The proposed project aims at developing an adaptable edge-computing node hardware architecture, featuring near-sensor reconfigurable signal processing acceleration units and memory units, along with the possibility of sharing computational workloads (i.e. program code and data) with other nodes and only ultimately with the cloud. The policy for defining the optimal microarchitecture, circuit and workload sharing parameters will be based on Artificial-Intelligence (AI) decision algorithms explored within the project, with genetic algorithms and support vector machine algorithm as first candidates.
The adaptability of the edge-node hardware architecture to the dynamic modification of the application and of the network composition is a key innovation. The innovation potential of the project is threefold: implementation of reconfigurable hardware microarchitecture in edge-computing nodes, implementation of workload sharing capability as an additional parameter in nodes' architecture reconfigurability, and the application of AI algorithms to tailor the configuration to the (dynamically changing) target application.
The adaptability of the IoT node hardware architecture to the dynamic modification of the application and of the network composition is a key innovation.
In fact, the conventional approach is to design the IoT architecture a priori as either edge computing based (where processing is closer to sensing devices) or cloud computing based (where computation is concentrated on servers). Any addition of services/devices requires a complete restructuring of the IoT architecture. The innovation allowed by the proposed project with respect to the current IoT implementation approach is on three levels:
1) The implementation of the capability of adapting the hardware microarchitecture of the node is an enabling technology that opens the way to the exploration of automatic adaptation (evolution) of IoT nodes, according to dynamic changes in the application workload and in the network composition.
2) Similarly, implementing the capability of sharing workload among local nodes as well as transferring it to the cloud, although not conceptually new, is interpreted in an innovative way as it is integrated as an additional parameter along with microarchitecture and electrical parameter reconfiguration. Workload sharing support will be totally transparent to the user and operating-system-agnostic.
3) The application of artificial intelligence techniques to evolve the nodes' microarchitecture and the workload share according to the requirements of the application and the network structure.
Therefore, the innovation potential of the proposed project is to realize a working infrastructure for the implementation of artificial-intelligence-configured clusters of IoT nodes.