# Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

arXiv: https://arxiv.org/abs/2007.05657 · HTML: https://mlsanity.com/paper/2007.05657/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2020.

## Built on by (most cited first, 30 of 34)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Energy-Latency Attacks via Sponge Poisoning](https://mlsanity.com/paper/2203.08147/) (2022)
- [Brain-inspired learning in artificial neural networks: a review](https://mlsanity.com/paper/2305.11252/) (2023)
- [Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence](https://mlsanity.com/paper/2106.01288/) (2021)
- [Spiking Neural Networks for Frame-based and Event-based Single Object Localization](https://mlsanity.com/paper/2206.06506/) (2022)
- [Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks](https://mlsanity.com/paper/2112.13896/) (2021)
- [Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey](https://mlsanity.com/paper/2203.06951/) (2022)
- [Autonomous Driving with Spiking Neural Networks](https://mlsanity.com/paper/2405.19687/) (2024)
- [Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review](https://mlsanity.com/paper/2209.15137/) (2022)
- [Spiking neural networks for nonlinear regression](https://mlsanity.com/paper/2210.03515/) (2022)
- [To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration](https://mlsanity.com/paper/2306.15749/) (2023)
- [Spiking Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/2406.19230/) (2024)
- [The fine line between dead neurons and sparsity in binarized spiking neural networks](https://mlsanity.com/paper/2201.11915/) (2022)
- [An energy-based model for neuro-symbolic reasoning on knowledge graphs](https://mlsanity.com/paper/2110.01639/) (2021)
- [Navigating Local Minima in Quantized Spiking Neural Networks](https://mlsanity.com/paper/2202.07221/) (2022)
- [Analysis of Power-Oriented Fault Injection Attacks on Spiking Neural Networks](https://mlsanity.com/paper/2204.04768/) (2022)
- [Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey](https://mlsanity.com/paper/2206.05394/) (2022)
- [SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network](https://mlsanity.com/paper/2310.06488/) (2023)
- [Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks](https://mlsanity.com/paper/2206.09951/) (2022)
- [Intelligence Processing Units Accelerate Neuromorphic Learning](https://mlsanity.com/paper/2211.10725/) (2022)
- [The SkipSponge Attack: Sponge Weight Poisoning of Deep Neural Networks](https://mlsanity.com/paper/2402.06357/) (2024)
- [Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays](https://mlsanity.com/paper/2206.12992/) (2022)
- [SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2404.19668/) (2024)
- [Naturalizing Neuromorphic Vision Event Streams Using GANs](https://mlsanity.com/paper/2102.07243/) (2021)
- [A decreasing scaling transition scheme from Adam to SGD](https://mlsanity.com/paper/2106.06749/) (2021)
- [Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures](https://mlsanity.com/paper/2201.06703/) (2022)
- [A Fully Memristive Spiking Neural Network with Unsupervised Learning](https://mlsanity.com/paper/2203.01416/) (2022)
- [Energy Backdoor Attack to Deep Neural Networks](https://mlsanity.com/paper/2501.08152/) (2025)
- [SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks](https://mlsanity.com/paper/2203.01426/) (2022)
- [Feature Attribution Explanations for Spiking Neural Networks](https://mlsanity.com/paper/2311.02110/) (2023)

## 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.