Abstract
Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications require meeting performance targets and resilience constraints due to the limited device area and hostile environments for operation. Numerous research articles have proposed the edge AI accelerator for satisfying the applications, but not all include full specifications. Most of them tend to compare the architecture with other existing CPUs, GPUs, or other reference research, which implies that the performance exposé of the articles are not comprehensive. Thus, this work lists the essential specifications of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. The actual performance, implementation, and productized examples of edge AI accelerators are released in this paper. We introduce the evaluation results showing the edge AI accelerator design trend about key performance metrics to guide designers. Last but not least, we give out the prospect of developing edge AI’s existing and future directions and trends, which will involve other technologies for future challenging constraints.
Highlights
Convolution neural network (CNN), widely applied to image recognition, is a machine learning algorithm
This paper has presented a survey of up-to-date edge artificial intelligence (AI) accelerators and coarse-grained reconfigurable array (CGRA) accelerators that can apply to image recognition systems and introduced the evaluation value E for both edge AI accelerators and CGRAs
CGRA architectures meet the evaluation value E of the existing prior art edge AI accelerators, which implies the potential suitability of CGRA architectures for running edge AI applications
Summary
Convolution neural network (CNN), widely applied to image recognition, is a machine learning algorithm. Designed as an immobile system is a specific feature of these edge AI systems These kinds of applications do not care about power consumption and size, they tend to be aware of data privacy more. Power-sensitive edge AI devices require a new customized and flexible AI hardware platform to implement arbitrary CNN algorithms for real-time computing with low power consumption. The constraints are generated by the limited power source such as battery and the portable feature, which causes the size of an edge AI system limited To address these issues, examining the three key features of the prior art of the accelerators tailored for edge AI devices is necessary for providing future design directions.
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