The security and privacy concerns along with the amount of data that is
required to be processed on regular basis has pushed processing to the edge of
the computing systems. Deploying advanced Neural Networks (NN), such as deep
neural networks (DNNs) and spiking neural networks (SNNs), that offer
state-of-the-art results on resource-constrained edge devices is challenging
due to the stringent memory and power/energy constraints. Moreover, these
systems are required to maintain correct functionality under diverse security
and reliability threats. This paper first discusses existing approaches to
address energy efficiency, reliability, and security issues at different system
layers, i.e., hardware (HW) and software (SW). Afterward, we discuss how to
further improve the performance (latency) and the energy efficiency of Edge AI
systems through HW/SW-level optimizations, such as pruning, quantization, and
approximation. To address reliability threats (like permanent and transient
faults), we highlight cost-effective mitigation techniques, like fault-aware
training and mapping. Moreover, we briefly discuss effective detection and
protection techniques to address security threats (like model and data
corruption). Towards the end, we discuss how these techniques can be combined
in an integrated cross-layer framework for realizing robust and
energy-efficient Edge AI systems.

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