Dynamic neuromorphic architecture selection scheme for intelligent Internet of Things services

With the development of Internet of Things (IoT)-related technologies and artificial intelligence (AI) technologies, various IoT services are becoming more intelligent, and their use range is increasing and diversifying. IoT hardware and IoT software must support AI-related functions to provide an intelligent IoT service. In general, IoT devices powered by batteries have limited computing performance when compared to general computing environments. Therefore, it is essential to provide AI-related functions at low power in IoT devices to implement and offer various intelligent services. Neuromorphic computing devices or neuromorphic computing architectures can operate with low power energy consumption. If applied to IoT devices, AI-related functions can be implemented in a resource-constrained IoT device environment. The proposed neuromorphic architecture abstraction (NAA) model dynamically selects the proper neuromorphic architecture by comparing the parameter size of a given SNN model. It also considers the specifications and error probability of the available neuromorphic architecture. We also implement the proposed model in a real IoT computing environment and show that the proposed NAA model and dynamic selection scheme can reduce the execution time for training and inferencing. It reduces the training and inferencing time of a given model compared with the method of randomly specifying the neuromorphic architecture.

Automated summary

With the advancement of IoT and AI technologies, intelligent IoT services are gaining popularity. To enable intelligent functions in resource-constrained IoT devices, the use of low-power neuromorphic computing devices/architectures is proposed. A model called Neuromorphic Architecture Abstraction (NAA) dynamically selects the appropriate architecture based on parameter size, specifications, and error probability. Experimental results demonstrate that the proposed NAA model reduces training and inferencing time compared to random architecture selection.