Data and code pertaining towards developing computation-in-memory based edge-AI solutions for healthcare
doi:10.4121/0c599860-79cc-42c7-921a-2f81c6430ab8.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
For a link that will always point to the latest version, please use
doi: 10.4121/0c599860-79cc-42c7-921a-2f81c6430ab8
doi: 10.4121/0c599860-79cc-42c7-921a-2f81c6430ab8
Datacite citation style:
Diware, Sumit Shaligram (2024): Data and code pertaining towards developing computation-in-memory based edge-AI solutions for healthcare. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/0c599860-79cc-42c7-921a-2f81c6430ab8.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
CC0
This repository contains data pertaining to my works towards developing computation-in-memory based edge-AI solutions for healthcare. They cover model development for two healthcare applications and mitigation strategies for three memristor non idealities, along with a prototype design.
history
- 2024-05-16 first online, published, posted
publisher
4TU.ResearchData
format
.py, .png, .txt, .tcl, .pt, .vhd, .pyc, cadence genus filetypes, cadence innovus filetypes, linux system filetypes
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Quantum & Computer Engineering
DATA
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8,626,828,175 bytes unzipped
