Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

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Abstract-Code Completion is one of the most popular IDE features for accessing APIs, freeing programmers from remembering specific details about an API and reducing keystrokes. We propose three ways to enhance the current code-completion systems to work more effectively with large APIs. First, we propose two methods for sorting APIs, by type hierarchy and by use count, and show that their use significantly reduces the number of API proposals a user must navigate while using Code Completion. Second, we show that context-specific filtering of inappropriate proposals can also reduce the number of proposals a user must navigate. Third, we propose to group API proposals by their functional roles, which can help maintain a well-ordered, meaningful list of API proposals in the presence of dynamic reordering. These functionalities are grouped into a research prototype, BCC (Better Code Completion). We evaluated fourteen configurations of BCC by simulating Code Completion nearly three million times on nine open-source Java projects that utilize AWT/Swing.