May 30, 2019
No cloud required: Why AI’s future is at the edge - SiliconANGLE
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No cloud required: Why AI’s future is at the edge - SiliconANGLE
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Posted by Sujith Ravi, Senior Staff Research Scientist, Google Expander Team Successful deep learning models often require significant a...
Successful deep learning models often require significant amounts of computational resources, memory and power to train and run, which presents an obstacle if you want them to perform well on mobile and IoT devices. On-device machine learning allows you to run inference directly on the devices, with the benefits of data privacy and access everywhere, regardless of connectivity. On-device ML systems, such as MobileNets and ProjectionNets, address the resource bottlenecks on mobile devices by optimizing for model efficiency. But what if you wanted to train your own customized, on-device models for your personal mobile application?
Yesterday at Google I/O, we announced ML Kit to make machine learning accessible for all mobile developers. One of the core ML Kit capabilities that will be available soon is an automatic model compression service powered by “Learn2Compress” technology developed by our research team. Learn2Compress enables custom on-device deep learning models in TensorFlow Lite that run efficiently on mobile devices, without developers having to worry about optimizing for memory and speed. We are pleased to make Learn2Compress for image classification available soon through ML Kit. Learn2Compress will be initially available to a small number of developers, and will be offered more broadly in the coming months. You can sign up here if you are interested in using this feature for building your own models.
How it Works
Learn2Compress generalizes the learning framework introduced in previous works like ProjectionNet and incorporates several state-of-the-art techniques for compressing neural network models. It takes as input a large pre-trained TensorFlow model provided by the user, performs training and optimization and automatically generates ready-to-use on-device models that are smaller in size, more memory-efficient, more power-efficient and faster at inference with minimal loss in accuracy.
!Learn2Compress for automatically generating on-device ML models.
To do this, Learn2Compress uses multiple neural network optimization and compression techniques including: Pruning reduces model size by removing weights or operations that are least useful for predictions (e.g.low-scoring weights). This can be very effective especially for on-device models involving sparse inputs or outputs, which can be reduced up to 2x in size while retaining 97% of the original prediction quality. Quantization techniques are particularly effective when applied during training and can improve inference speed by reducing the number of bits used for model weights and activations. For example, using 8-bit fixed point representation instead of floats can speed up the model inference, reduce power and further reduce size by 4x. Joint training and distillation approaches follow a teacher-student learning strategy — we use a larger teacher network (in this case, user-provided TensorFlow model) to train a compact student network (on-device model) with minimal loss in accuracy.
Hemang Patel's answer: TensorFlow Lite is a lightweight version of TensorFlow, designed to bring machine learning (ML) models to mobile, emb
TensorFlow Lite is a lightweight version of TensorFlow, designed to bring machine learning (ML) models to mobile, embedded, and edge devices. It enables fast, efficient, and offline AI experiences—without depending on cloud servers.
Lesson 2
Adding Audio classification to your mobile app simple tracking wawes size windows and the end adding tensorflow lite task library.
*NB: still by the taken capture there dog on microphone tool
Tensorflow Lite
The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices.
The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era
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edge computing
The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices.
The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era
large scale IoT deployments
The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices.
The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era
edge computing
The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices.
https://www.mdpi.com/1999-5903/14/12/363