Nature, 502(7470):172–172, 2013. 文章来源申明:本篇文章来自:EFFICIENT METHODS AND HARDWARE FOR DEEP LEARNING-augmented 可以在 公众号『运筹OR帷幄』后台 回复关键词:“韩松论文”获取由我平台编辑精心整理的韩松论文资料,如果觉得有用, 请勿吝啬你的留言和赞哦!~...
Deep Learning Research, Hardware, Kaggle | Interview with Tim Dettmers | Chai 20 -- 1:20:27 App The Ancient Secrets of Computer Vision - 11 - More Machine Learning for Comput 33 -- 45:53 App Ed Feigenbaum's Search for A.I. 81 -- 58:09 App Applying deep learning to credit scoring...
In developing Deep Speech 2, Baidu also created new hardware architecture for deep learning that runs seven times faster than the previous version. Deep learning usually relies on graphics processors, because these are good for the intensive parallel computations involved. The speed achieved “all...
蔡涵,MIT二年级在读博士生。他的研究方向主要包括高效深度学习 (Efficient Deep Learning) 及自动机器学习 (AutoML)。他开发的硬件感知 (hardware-aware) 自动机器学习框架 (ProxylessNAS, Once-for-All) 可以自动为目标硬件平台和效率约束设计专用的神经网络结构,从而大幅提升神经网络在目标硬件上的性能与效率,多次获...
At a company event in San Jose, he said, “For the first time we designed a [graphics-processing] architecture dedicated to accelerating AI and to accelerating deep learning.” Nvidia spent more than $2 billion on R&D to produce the new chip, said Huang. It has a total of 15 billion ...
他的研究方向主要包括高效深度学习 (Efficient Deep Learning) 及自动机器学习 (AutoML)。他开发的硬件感知 (hardware-aware) 自动机器学习框架 (ProxylessNAS, Once-for-All) 可以自动为目标硬件平台和效率约束设计专用的神经网络结构,从而大幅提升神经网络在目标硬件上的性能与效率,多次获得低功耗计算机视觉竞赛奖项 (...
他的研究方向主要包括高效深度学习 (Efficient Deep Learning) 及自动机器学习 (AutoML)。他开发的硬件感知 (hardware-aware) 自动机器学习框架 (ProxylessNAS, Once-for-All) 可以自动为目标硬件平台和效率约束设计专用的神经网络结构,从而大幅提升神经网络在目标硬件上的性能与效率,多次获得低功耗计算机视觉竞赛奖项 (...
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning(NeurIPS'20) Once for All: Train One Network and Specialize it for Efficient Deployment(ICLR'20) ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware(ICLR'19) ...
比如说AlexNet,60M parameter,我把它减到6-7M parameter。但是可以实现同样的准确率。这样的话,从源头上就可以把这样的问题做简化。然后再做efficient hardware architecture。这样从algorithm &hardware co-design,这样的design 空间非常大,也可以彻底解决问题。
The evolution of machine learning But in recent years small start-ups and big companies alikehave been modifyingtheir chip architecture to meet the demands of new artificial intelligence workloads, including autonomous driving and speech recognition. Two years ago, according to Deloitte, almost all...