TR236: Edge Computing for Smart Buildings
Abstract/Contents
- Abstract
- The state of the art in computer hardware has not kept up with the astonishing progress rate and requirements of deep learning and inference techniques for vision-based smart buildings. As a result, existing systems are not scalable, since they pipe unmanageable amounts of raw data from local sensors to remote servers. To prevent this data deluge, custom hardware for smart building solutions must be developed. The most suitable approach will process the raw data locally and forwards only relevant information to a central network that fuses this information for inference and action planning. We will leverage our expertise in hardware design to devise a custom solution for vision-based smart buildings. Our work will use prior work by Li & Milstein (Stanford CS & SoM) as a baseline. While we will initially work with semi-programmable chips (GPUs/FPGAs) to design and evaluate the local processing system, the long-term goal is to develop silicon chips that will be as efficient as the custom processors found in modern cell phones.
Description
| Type of resource | text |
|---|---|
| Date created | November 2019 |
Creators/Contributors
| Author | Chai, Elaina | |
|---|---|---|
| Author | Murmann, Boris |
Subjects
| Subject | Smart Buildings |
|---|---|
| Subject | Machine Learning |
| Subject | Edge Computing Hardware |
| Subject | Hardware-Software Complexity Tradeoffs |
| Genre | Technical report |
Bibliographic information
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- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
Preferred citation
- Preferred Citation
- Chai, Elaina and Murmann, Boris. (2019). TR236: Edge Computing for Smart Buildings. Stanford Digital Repository. Available at: https://purl.stanford.edu/ns297py3379
Collection
CIFE Publications
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- cife-email@stanford.edu
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