Guest Editorial: Alternative Computing and Machine Learning for Internet of Things

Abstract

The impending Internet of Things (IoT) wave is promising to affect every aspect of our daily lives, ranging from smart things to smart buildings, smart cities, and smart environments. A lot of attention has been devoted to the tsunami of data produced by IoT, and the related means of extracting useful actionable information from it, spawning efforts in Big Data processing and machine learning. Yet, all of this does little to address the need for IoT to capture, interpret, and act on this wall of (noisy) information at the right time, at the right place, and in the right form. Conventional computing systems are a poor match to the needs of this emerging massively distributed real-time system. Hence, alternative computing techniques present an attractive alternative, trading off computational resolution for significant gains in quality-of-service energy efficiency and robustness. This observation is based on the conjecture that most applications related to IoT have an inherent error resilience and are evolutionary (that is, learning-based). Alternative computing strategies may be conceived at every level of the design hierarchy, starting from the device level with novel 3-D nonvolatile memory/logic combinations, or at the architectural level by shifting away from the traditional von Neumann architecture to different computing paradigms such as neuromorphic and/or stochastic computation all the way up to the algorithmic and data representation levels.