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Affordable AI Connects To A Better Life
- 1. Affordable AI Connects To A Better Life Bofu Chen, Sep 21, 2016
- 2. Intelligent Gateway Affordable AI Techniques Implementation Example: Pepper Robot Example: Campus Security System AGENDA
- 6. Cat Recognition Photo: Robert Bond
- 8. Deep Learning Inference Cat! No Backpropagation
- 9. Inference Essentials MB Computing Time Memory Usage Shorten the prediction time is always welcome Device memory is limited, but deep learning model can be huge
- 10. Techniques To Make AI Affordable
- 11. Inference Researches Weight Storage Hardware Usage Reduce weight storage size without sacrificing accuracy Utilize computing components (CPU, GPU, etc.) as many as possible simultaneously Binarized Neural Networks, http://arxiv.org/abs/1602.02830 | XNOR-Net, http://arxiv.org/abs/1603.05279 | DoReFa-Net, https://arxiv.org/abs/1606.06160 | DeepX, http://niclane.org/pubs/deepx_ipsn.pdf
- 12. Approaches Compression Nvidia TensorRT Optimization Throughput Power efficiency Memory usage Keep accuracy Speed up Low-level speed up
- 13. Nvidia TensorRT Like a model compiler Production Deep Learning with NVIDIA GPU Inference Engine, https://devblogs.nvidia.com/parallelforall/production-deep-learning-nvidia-gpu-inference-engine/
- 14. Pruning Learning both Weights and Connections for Efficient Neural Networks, https://arxiv.org/abs/1506.02626
- 15. Quantization How to Quantize Neural Networks with TensorFlow, https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/how_tos/quantization/index.md DNN is noise tolerable FP16 to INT8 Hardware speedup FPU to ALU
- 16. Inference Without DL Frameworks Likely A compiler intermediate representation for image recognition and heterogeneous computing, http://liblikely.org/
- 17. Implementation
- 18. Deep Learning Computer Vision NVIDIA TX1 Pre-trained On Server
- 19. End Devices (Sender) Architecture End Devices (Receiver) Intelligent Gateway NVIDIA TX1 Ubuntu Tensorflow REST TensorRT gRPC
- 20. Inference Choices TensorRT Fast Object Slow Motion
- 21. TensorFlow on TX1 DONE Model Server Maximize Performance NEXT Inference Optimization on Ubuntu
- 22. Other Attempts Raspberry Pi 3 Qualcomm Snapdragon 801 0.9s/img GoogLeNet Real-Time Inception v3
- 23. Pepper, The Emotional Robot
- 25. HW Specification 4-core 1.9 GHz 4 GB 790 MHz Pepper motherboard specification, http://doc.aldebaran.com/2-4/family/pepper_technical/motherboard_pep.html
- 26. Vision and Speech Limitations Instead of face identification Keywords instead of NLP FaceRecognition SpeechRecognition
- 27. Cloud Solution Drawbacks CostConnectivity Privacy Need to ensure bandwidth, stability and latency are good enough Huge amount of image transmission You might want to keep family information locally
- 30. Real World Gesture Recognition Algorithm
- 55. Violent Event
- 56. Kinect v2 Update USB Firmware Open Source Libraries Fix data transmission issue libfreenect2 and pylibfreenect2 make enablement easier MS Kinect v2 on Nvidia Jetson TX1, http://jetsonhacks.com/2016/07/11/ms-kinect-v2-nvidia-jetson-tx1/
- 57. We Are DT42