# Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study

arXiv: https://arxiv.org/abs/2209.01507 · HTML: https://mlsanity.com/paper/2209.01507/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 3 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics](https://mlsanity.com/paper/1608.02989/) (2016)
- [Design Space Exploration of Algorithmic Multi-Port Memories in High-Performance Application-Specific Accelerators](https://mlsanity.com/paper/2007.09363/) (2020)
- [NV-Fogstore : Device-aware hybrid caching in fog computing environments](https://mlsanity.com/paper/2010.10562/) (2020)

## Abstract (arXiv)

Deep learning research has generated widespread interest leading to emergence of a large variety of technological innovations and applications. As significant proportion of deep learning research focuses on vision based applications, there exists a potential for using some of these techniques to enable low-power portable health-care diagnostic support solutions. In this paper, we propose an embedded-hardware-based implementation of microscopy diagnostic support system for PoC case study on: (a) Malaria in thick blood smears, (b) Tuberculosis in sputum samples, and (c) Intestinal parasite infection in stool samples. We use a Squeeze-Net based model to reduce the network size and computation time. We also utilize the Trained Quantization technique to further reduce memory footprint of the learned models. This enables microscopy-based detection of pathogens that classifies with laboratory expert level accuracy as a standalone embedded hardware platform. The proposed implementation is 6x more power-efficient compared to conventional CPU-based implementation and has an inference time of ~ 3 ms/sample.