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Scaling Deep-Learning Pneumonia Detection Inference on a Reconfigurable PlatformJangkun Wang, Khanh N. Dang and Abderazek Ben Abdallah, “Scaling Deep-Learning Pneumonia Detection Inference on a Reconfigurable Self-Contained Hardware Platform”, 2023 IEEE 6th International Conference on Electronics Technology (ICET), May 12-15, 2023. 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 1.
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.
- Wang, Jiangkun, Ogbodo Mark
Ikechukwu, Khanh N. Dang, and Abderazek Ben
Abdallah. 2022. "Spike-Event X-ray Image
Classification for 3D-NoC-Based Neuromorphic
Pneumonia Detection" Electronics 11, no. 24:
4157.
https://doi.org/10.3390/electronics11244157
The success of deep learning in extending the frontiers of artificial intelligence has accelerated the application of AI-enabled systems in addressing various challenges in different fields. In healthcare, deep learning is deployed on edge computing platforms to address security and latency challenges, even though these platforms are often resource-constrained. Deep learning systems are based on conventional artificial neural networks, which are computationally complex, require high power, and have low energy efficiency, making them unsuitable for edge computing platforms. Since these systems are also used in critical applications such as bio-medicine, it is expedient that their reliability is considered when designing them. For biomedical applications, the spatio-temporal nature of information processing of spiking neural networks could be merged with a fault-tolerant 3-dimensional network on chip (3D-NoC) hardware to obtain an excellent multi-objective performance accuracy while maintaining low latency and low power consumption. In this work, we propose a reconfigurable 3D-NoC-based neuromorphic system for biomedical applications based on a fault-tolerant spike routing scheme. The performance evaluation results over X-ray images for pneumonia (i.e., COVID-19) detection show that the proposed system achieves 88.43% detection accuracy over the collected test data and could be accelerated to achieve 4.6% better inference latency than the ANN-based system while consuming 32% less power. Furthermore, the proposed system maintains high accuracy for up to 30% inter-neuron communication faults with increased latency  - Patent: Abderazek Ben Abdallah,
Huankun Huang, Nam Khanh Dang, Jiangning Song,
"ＡＩプ ロセッサ," 特願2020-194733 (2020 年11月24日)
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Embedded Multicore SoC Architecture and Design for Real-time ECG ProcessingRecent technological advances in wireless networking, microelectronics and the Internet allow us to fundamentally change the way elderly health care services are practiced. Traditionally, embedded personal medical monitoring systems have been used only to collect data. Data processing and analysis are performed off-line, making such devices impractical for continual monitoring and early detection of medical disorders. The goal of this project is to research about efficient novel in-body snart embedded system to effectively monitor elderly health status remotely. In particular, we investigate an extreme area in the design space of networked embedded objects: the domain of low energy, and real-time. Issues related to the design, implementation and deployment of such systems are also studied. |
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Low-Power Queue Processor Architecture and DesignThis project focuses on the research about a novel low power and high performance parallel processor processor based on Queue computation model, where Queue programs are generated by traversing a given data flow graph using level order traversal. The Queue processor uses a circular queue-register to manipulate lates operands
and results, and exploits parallelism dynamically with
"little efforts" when compared with conventional
architectures. The nonexistence of false dependencies
allows programs to expose maximum parallelism that the
queue processor can execute without complex and
power-hungry hardware such as register renaming and
large instruction windows. Parallel processing allows
queue processors to speed-up the execution of
applications. We are researching and developing a
complete tool-chain for this promising computing model
consisting of: compiler, assembler, functional and cycle
accurate simulator, and hardware design.
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Contact:
Abderazek Ben Abdallah (E-mail:
benab@u-aizu.ac.jp)
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