Along with the development of artificial intelligence technology, real-time algorithms for these AI need to be considered.
In particular, we are researching and developing real-time algorithms specialized for Deep Neural Networks that are often used in AI.
Our researches are focused on DNN optimization that guarantees real-time under limited resource situations, network reconfiguration using the characteristics of DNN, and more.

 • LaLaRAND: Flexible Layer-by-Layer CPU/GPU Scheduling for Real-Time DNN Tasks (RTSS 2021)
In a situation where several DNNs under various types of computing (heterogeneous) resources, schedule them in player-granularity and allocate them to appropriate resources to achieve high accuracy and guarantee real-time.

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  • DNN-SAM: Split-and-Merge DNN Execution for Real-Time Object Detection (RTAS 2022)
According to the characteristics of DNN, an existing DNN structure can be changed, and real-time can be guaranteed through it.
In an object detection network, the importance of the entire image may not be the same. Therefore, we devised a new computational model, such as detecting important parts separately to retrieving results of the critical region quickly and accurately, and developing an algorithm to guarantee real-time.

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