Scaling Deep-Learning Pneumonia Detection Inference on a Reconfigurable Self-Contained Hardware Platform

Artificial Intelligence (AI) has been used in applications to alleviate specific problems in academia and industry. For instance, in healthcare, where edge-based computing platforms are heavily used, when it comes to latency and security issues, the increased demands of application of AI applications such as deep learning require a specific platform to meet the latency, security, and power consumption challenges. This work presents methods and architectures for scaling deep learning inference for pneumonia detection in chest X-ray images based on a reconfigurable self-contained hardware platform named AIRBiS
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. The performance evaluation results show that the proposed approach achieves 95.2% detection accuracy of pneumonia over the collected test data with the computer-aided diagnosis scenario. The secure collaborative-learning approach achieves comparable accuracy to the conventional training scenario. However, for rapid batch detection, the detection could be accelerated by 0.023s. Moreover, the system inference acceleration is 13 times (on average) more energy-efficient than conventional approaches.
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AIRBiS project, the University of Aizu: https://www.uaizu.ac.jp/misc/benablab/airbis.html