参考 Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
处理器IP厂商的on-device机器学习方案 先回顾一下终端设备机器学习(on-device machine learning)的一些背景。 问题1:为什么要做on-device的机器学习? on-device是和cloud相对的。在终端设备上做机器学习应用主要是做inference,也就是用训练(训练的过程一般是在cloud端或者处理能力强的GPU上完成)好的模型解决实际问题。
Federated Learning通过device之间传递gradients,不传递data来保证data privacy,但是Deap Leakage可以通过gradients倒推原数据,仍然有数据安全问题。增加Gaussian/laplcian noise来抵御可能会导致模型表现下降,但是梯度压缩可以防止leakage问题。On Device训练的bottleneck是memory,为此有TinyTL的方式,使用fine-tune bias + lite ...
Thus, the device may perform supervised on-device learning based on the remote NLU data. The device may determine differences between the updated speech processing model and an original speech processing model received from the remote system and may send data indicating these differences to the ...
Device-Based Inferencing Keep It Simple Connect(); 2017 Volume 32 Number 13 Machine Learning - Deliver On-Device Machine Learning Solutions By Larry O'Brien | Connect(); 2017 You’ve read the headlines: Artificial intelligence and machine learning (AI/ML) technologies are rewriting the bench...
Device-Based Inferencing Keep It Simple Connect(); 2017 Volume 32 Number 13 Machine Learning - Deliver On-Device Machine Learning Solutions By Larry O'Brien | Connect(); 2017 You’ve read the headlines: Artificial intelligence and machine learning (AI/ML) technologies are rewrit...
When you're done, be sure to return to this page so you can continue learning.In Settings, Endpoints, Device management, Onboarding select operating system dropdown to see the supported options.After you select the appropriate operating system option, the supported deployment options are outlined....
Both the on-device and server models use grouped-query-attention. We use shared input and output vocab embedding tables to reduce memory requirements and inference cost. These shared embedding tensors are mapped without duplications. The on-device model uses a vocab size of 49K, while the serve...
Deep learning has facilitated human-level performance on several tasks spanning a multitude of domains such as computer vision, natural language processing, medical analysis, gaming, retail, and marketing, just to name a few. The ability to solve a problem end-to-end, learn self-supervised high...
which allows users to capture and build their own RSSI maps by off-line training of a set of selected classifiers and using the models generated to obtain the current indoor location of the target device. In an early experimental and design stage, 59 classifiers were evaluated, using data from...