mlsanityData to 7 Oct 2026

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

arXiv 2007.05657 ↗First posted 11 Jul 2020cs.AR cs.LG eess.SPcited by 34

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Abstract (arXiv)

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. The tutorial is augmented with case studies of the vast literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hardware platforms by performing a sensor fusion signal processing task combining electromyography (EMG) signals with computer vision. Comparisons are made between dedicated neuromorphic processors and embedded AI accelerators in terms of inference latency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that various accelerators and neuromorphic processors introduce to healthcare and biomedical domains.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jul 2020.

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Citing papers per year: 2021: 6; 2022: 15; 2023: 4; 2024: 5; 2025: 2; 2026: 2.

34 papers.

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  1. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  2. 2022Energy-Latency Attacks via Sponge Poisoningcited by 29
  3. 2023Brain-inspired learning in artificial neural networks: a reviewcited by 26
  4. 2021Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligencecited by 21
  5. 2022Spiking Neural Networks for Frame-based and Event-based Single Object Localizationcited by 18
  6. 2021Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networkscited by 15
  7. 2022Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Surveycited by 14
  8. 2022Generalizable machine learning for stress monitoring from wearable devices: A systematic literature reviewcited by 12
  9. 2024Autonomous Driving with Spiking Neural Networkscited by 12
  10. 2022Spiking neural networks for nonlinear regressioncited by 10
  11. 2023To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Accelerationcited by 10
  12. 2024Spiking Convolutional Neural Networks for Text Classificationcited by 10
22 more
  1. 2022The fine line between dead neurons and sparsity in binarized spiking neural networkscited by 7
  2. 2021An energy-based model for neuro-symbolic reasoning on knowledge graphscited by 4
  3. 2022Navigating Local Minima in Quantized Spiking Neural Networkscited by 4
  4. 2022Analysis of Power-Oriented Fault Injection Attacks on Spiking Neural Networkscited by 4
  5. 2022Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Surveycited by 4
  6. 2023SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Networkcited by 4
  7. 2022Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networkscited by 3
  8. 2022Intelligence Processing Units Accelerate Neuromorphic Learningcited by 3
  9. 2024The SkipSponge Attack: Sponge Weight Poisoning of Deep Neural Networkscited by 3
  10. 2022Gradient-based Neuromorphic Learning on Dynamical RRAM Arrayscited by 2
  11. 2024SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networkscited by 2
  12. 2021Naturalizing Neuromorphic Vision Event Streams Using GANscited by 1
  13. 2021A decreasing scaling transition scheme from Adam to SGDcited by 1
  14. 2022Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architecturescited by 1
  15. 2022A Fully Memristive Spiking Neural Network with Unsupervised Learningcited by 1
  16. 2025Energy Backdoor Attack to Deep Neural Networkscited by 1
  17. 2022SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networkscited by 0
  18. 2023Feature Attribution Explanations for Spiking Neural Networkscited by 0
  19. 2024MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signalscited by 0
  20. 2025Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognitioncited by 0
  21. 2026Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Trainingcited by 0
  22. 2026EEG-Based Motor Imagery BCI Algorithms and Technologies: A Reviewcited by 0