Fitting AI Models for Embedded Deployment
AI is no longer limited to powerful computing environments such as GPUs or high-end CPUs, and is often integrated into systems with limited resources like patient monitoring, diagnostic systems in vehicles, and manufacturing equipment. Fitting AI onto hardware with limited memory and power supply requires deliberate trade-offs between size of model, accuracy, inference speed, and power consumption—and that process is still challenging in many frameworks for AI development.
Optimizing AI models for limited hardware generally proceeds in these three steps:
- Model Selection: Identify less complex models and neural networks that still achieve the required accuracy
- Size Reduction: Tune the hyperparameters to generate a more compact model or prune the neural network
- Quantization: Further reduce size by quantizing model parameters
Additionally, especially for signal and text problems, feature extraction and selection result in more compact models. This talk demonstrates model compression techniques in MATLAB® and Simulink® by fitting a machine learning model and pruning a convolutional network for an intelligent hearing aid.
Published: 25 May 2022
Related Products
Learn More
Featured Product
Deep Learning Toolbox
Up Next:
Related Videos:
웹사이트 선택
번역된 콘텐츠를 보고 지역별 이벤트와 혜택을 살펴보려면 웹사이트를 선택하십시오. 현재 계신 지역에 따라 다음 웹사이트를 권장합니다:
또한 다음 목록에서 웹사이트를 선택하실 수도 있습니다.
사이트 성능 최적화 방법
최고의 사이트 성능을 위해 중국 사이트(중국어 또는 영어)를 선택하십시오. 현재 계신 지역에서는 다른 국가의 MathWorks 사이트 방문이 최적화되지 않았습니다.
미주
- América Latina (Español)
- Canada (English)
- United States (English)
유럽
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)
아시아 태평양
- Australia (English)
- India (English)
- New Zealand (English)
- 中国
- 日本Japanese (日本語)
- 한국Korean (한국어)