Hardware Accelerator Design for Healthcare Applications: Review and Perspectives

Tripathi, Jai Narayan; Kumar, Binod; Junjariya, Dinesh

doi:10.1109/iscas48785.2022.9937920

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Cited by 3 publications

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“…Different signal kinds or computing tasks may respond better to various encoding strategies. [195][196][197] Neuromorphic computing also requires a careful consideration of the degree of the complexity of the encoding scheme, as well as the hardware resources available for implementing the decoding operations. Ultimately, the choice of encoding and decoding techniques should be tailored to the specific computing task and the available hardware resources.…”

Section: Materials Advancesmentioning

confidence: 99%

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Prakash,

Gupta,

Mehta

et al. 2023

Mater. Adv.

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Section: Materials Advancesmentioning

confidence: 99%

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Prakash,

Gupta,

Mehta

et al. 2023

Mater. Adv.

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Point Calculation Detection System Based on In Suit Convolution Transistor for Clinical Diagnosis of Heart Disease

Cheng,

Zhang,

et al. 2024

Adv Funct Materials

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Electrocardiograph signals reflect the current state of the heart and have great significance to the clinical diagnosis of the heart. Convolutional neural networks perform excellently in electrocardiograph pattern recognition. However, CNNs processing ECG signals need to convert them from 1D to 2D, leading to additional circuit and time costs in hardware. Here, a convolution organic transistor (COT) is proposed for monitoring the ECG with CNNs. Based on the surface electric field effect and trap effect, COT can directly process 1D ECG data without complex preprocessing. It can complete the convolution calculation of ECG signals ≈20 000 times per second in theory and reduce the number of devices by 83% compared to conventional arrays. Further, actual ECG signals are measured and input into the COT, which can initially recognize the type of ECG abnormality. Finally, a point calculation detection system is established with 96.2% recognition accuracy in the five‐heartbeat classification task by combining the 1D CNN.

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Lightweight machine learning for privacy-preserving and secure networked medical devices: The SEPTON project use cases

Messinis

Protonotarios

Tzortzis

et al. 2023

Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments

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Hardware Accelerator Design for Healthcare Applications: Review and Perspectives

Cited by 3 publications

References 43 publications

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Point Calculation Detection System Based on In Suit Convolution Transistor for Clinical Diagnosis of Heart Disease

Lightweight machine learning for privacy-preserving and secure networked medical devices: The SEPTON project use cases

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