One of the reasons we have written so much about Chinese search and social web giant, Baidu, in the last few years is because they have openly described both the hardware and software steps to making deep learning efficient and high performance at scale. …
In the U.S. it is easy to focus on our native hyperscale companies (Google, Amazon, Facebook, etc.) …
When it comes to solving deep learning cluster and software stack problems at scale, few companies are riding the bleeding edge like Chinese search giant, Baidu. …
When it comes to deep learning innovation on the hardware front, few other research centers have been as forthcoming with their results as Baidu. …
While much of the work at Baidu we have focused on this year has centered on the Chinese search giant’s deep learning initiatives, many other critical, albeit less bleeding edge applications present true big data challenges. …
This month Nvidia bolstered its GPU strategy to stretch further into deep learning, high performance computing, and other markets, and while there are new options to consider, particularly for the machine learning set, it is useful to understand what these new arrays of chips and capabilities mean for users at scale. …
“AI is transforming the entire world of technology. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. …
If the last year of stories here from research labs at the forefront of deep learning hasn’t made it clear, the accelerator of choice for training the models that will feed the next generation of speech and image recognition (not to mention a wealth of other application areas) is certainly GPUs. …
While many consumers in the U.S. might not have heard much about Baidu, when it comes to engineers and computer scientists, the Chinese company is on par with Google, Facebook, and their ilk when it comes to massively scaled distributed computing. …
Open source hardware is something that is intellectually satisfying as well as economically rewarding, but it is clearly not something for everyone. …
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