## 1 Introduction
Furthermore, numerous natural hazards applications have adopted and applied the edge AI system in several hard-to-reach fields, such as space or nuclear power stations. These deployment scenarios are more critical than usual consumer products. It has been seen that the usage of robotics deals with the radiation leakage of nuclear power stations [2] and remote sensing satellites in space [3]. Devices that work in these fields should have the self-fault elimination ability to tolerate the radiation since these critical environments can cause a system failure. As a result, edge AI devices targeting hard-to-reach environments should meet the abovementioned requirements and demonstrate fault tolerance to guarantee reliability. Regarding fault tolerance, a technique called dynamic reconfiguration plays a vital role, and is leveraged in the design to bypass the faulty modules. Overall, the PSS edge AI systems targeting hard-to-reach fields require the following conditions: (I) Size limitation, (II) Power source limitation, (III) Real-time and no internet coverage, and (IV) Fault tolerance. Conditions (I) to (III) are the three key features of edge AI accelerators. This paper mainly targets designing a competitive convolution engine module for edge AI accelerators to meet conditions (I) to (IV). A convolution engine is an essential module in an edge AI accelerator system and contains control units, convolution engines, memories, etc. In order to provide a convolution engine architecture for a PSS edge AI accelerator to meet the designing trend, we did an investigation [4], an algorithm pilot study [5], and a trial study [6]. The introduction of the three preliminary studies is describedin three paragraphs below. The paragraphs clarify what has been done and the contribution of this paper. The refine content of [4] added two recent state-of-the-art works, which will be disclosed in Section 2, design trend of edge AI accelerators. The investigation shows that the low-power ultra-small (LPUS) edge AI accelerators fall into the same scope regarding the three key features, which are said to be hundreds of Giga operation per second (GOPs) ability, with less than 10 mm\({}^{2}\), and below a watt power consumption. This LPUS scope is the scenario for this paper. Several state-of-the-art works [7; 8; 9] show that an efficient dataflow architecture can reduce an accelerator's area size and power consumption. [PERSON] et al. [7] use dataflow-aware architecture in the convolution engine to meet the area and power consumption limitation for the PSS edge AI accelerators. Since a convolution engine plays a primary role in an edge accelerator, we decided to design a dataflow-aware oriental architecture. Because the work [7] stands out in the PSS edge AI scope (LPUS scope) and has been commercialized [4], this paper uses [7] as a cross-reference to evaluate the proposed convolution engine architecture within the LPUS scope containing [7; 8; 10; 11]. The detail of why this work chose [7] as a 'targeting reference' among state-of-the-art works will be refined in the trend of the design chapter. After a scenario scope and reference target have been set, the following task is the dataflow of the mapping algorithm pilot study [5], which has shown a competitive result. A convolution engine should be able to process the images or data with different CNN algorithm variables, such as filter size or stride. After the algorithm has been designed, a preliminary showing the idea of the hardware, the trial study [6] implements a 48-processing element array of the convolution engine, a smaller scale of a complete size design in this paper without an adder module. The trial study mainly evaluates the reconfiguration bus within the PE array and shows the potential ability to compete with a scale-down PE array of [7] in terms of power and area size. However, they do not include the adder module, an essential component in a convolution engine, and do not set any clock constraints, which makes the synthesis result different from this paper.