Programmable and Semantic Connector for DNN Component Integration: a Software Engineering Perspective

As deep learning technology continues to evolve, deep neural network (DNN) models have found their way into numerous modern software applications and systems, serving as crucial components. Despite the widespread adoption of DNN models in software, their development process still largely adheres to a craft production model [1]. This craft production approach leads to the creation of unique, highly specialized DNN models that may excel within their target software but prove difficult to standardize or adapt for compatibility with other software systems. In addition, due to the holistic training of these crafted DNN models, they cannot be easily disassembled or reassembled to accommodate new software requirements. Consequently, the reuse of DNN models remains a significant challenge in software engineering, hindering the potential for greater efficiency and adaptability in the development process.