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tinyML. Talks Enabling ultra-low Power Machine Learning at the Edge "Once-for-All: Train One Network and Specialize it for Efficient Deployment"
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Train One Network and Specialize It for Efficient Deployment
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- 1 tinyML. Talks Enabling ultra-low Power Machine Learning at the Edge "Once-for-All: Train One Network and Specialize it for Efficient Deployment"
- 2 Our 1st generation solution
- 3 New Challenge: Efficient Inference on Diverse Hardware Platfo
- 4 Our new solution, OFA: Decouple Training and Search
- 5 Challenge: Efficient Inference on Diverse Hardware Platforms
- 6 Once-for-All Network: Decouple Model Training and Architecture Design
- 7 Solution: Progressive Shrinking
- 8 Connection to Network Pruning
- 9 Performances of Sub-networks on ImageNe
- 10 How about search?
- 11 Accuracy & Latency Improvement
- 12 More accurate than training from scratch
- 13 OFA: 80% Top-1 Accuracy on ImageNet
- 14 Specialized Architecture for Different Hardware Platform
- 15 AutoML Outperforms Human Designing better MLM 1st place in CVPR 19 Visual Wake Words Challenge
- 16 What if we also optimize the compiler and run
- 17 How to save CO2 emission
- 18 OFA for FPGA Accelerators
- 19 Next tiny ML Talk
