Arrhythmia Analysis Accelerator : A-Cube
We propose the A-Cube design methodology to create medical decision support on the edge. The design and implementation of an atrial fibrillation detector hardware core was selected as a proof-of-concept study. To facilitate the required atrial fibrillation functionality, we adopted an established AI model, based on Long Short-Term Memory (LSTM) technology for hardware implementation. The adaptation was done by varying design parameters such as data window and the number of LSTM units. We found that a data window of 40 beats and 20 LSTM units are sufficient to achieve a classification accuracy of 99.02%. We are confident that the A-Cube methodology can be used to implement this model in hardware. Doing so, will create a low power and low latency atrial fibrillation monitoring solution which has the potential to extend the observation duration while being convenient for patients.
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Comments
Hi, you just need to save to…
Hi, you just need to save to Editorial and not Drafts to get it submitted. You can come back later and change.
Adding initial milestones
Hi,
Great to see the project live. It is very easy to add the initial milestones and you can come back as each milestone progresses and make updates.
IITH are a good example of milestones in their project https://soclabs.org/project/real-time-edge-ai-soc-high-speed-low-complexity-reconfigurable-scalable-architecture-deep
You can simply select milestones from the generic design flow stages or create any unique milestones.
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Abstract
We propose the A-Cube design methodology to create medical decision support on the edge. The design and implementation of an atrial fibrillation detector hardware core was selected as a proof-of-concept study. To facilitate the required atrial fibrillation functionality, we adopted an established AI model, based on Long Short-Term Memory (LSTM) technology for hardware implementation. The adaptation was done by varying design parameters such as data window and the number of LSTM units. We found that a data window of 40 beats and 20 LSTM units are sufficient to achieve a classification accuracy of 99.02%. We are confident that the A-Cube methodology can be used to implement this model in hardware. Doing so, will create a low power and low latency atrial fibrillation monitoring solution which has the potential to extend the observation duration while being convenient for patients.