Architectures for Self-Powered Edge Intelligence

Abstract

Artificial intelligence (AI) and machine learning (ML)-based decision-making is proliferating to application spaces with dynamic and evolving inputs such as Internet of things (IoTs). The need for real-time decision-making in such applications requires the edge devices in IoT networks to possess in situ intelligence processing capability. Edge intelligence in the networks is critical to avert unpredictable latency of an otherwise cloud-based intelligence processing. Edge intelligence in IoTs also minimizes their energy demand by avoiding raw data transmission and better preserving data privacy by only transmitting actionable information. Meanwhile, due to form factor and cost constraints and battery-powered operation, the energy budget and computing/storage resources for edge intelligence are very limited in a typical IoT node. Addressing such computational challenges in IoTs, in this chapter, an architectural framework for self-powered edge intelligence is reviewed. First, architectural techniques are reviewed to exploit sensors in IoTs to harvest energy from their environment to sustain local intelligence processing. Next, architectures that can identify and focus on regions of interest (ROI) are discussed to exploit sparsity in input and to minimize edge intelligence workload. Finally, learning-based architectures are discussed to reduce power wastage, such as due to leakage power. With a synergistic integration of the above architectural techniques, many IoTs can leverage self-powered edge intelligence to heighten awareness of their application domains.KeywordsEnergy scavengingImage sensorsPower-gatingRegion of interest (ROI) identificationLow-power computingReconfigurable computing