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"Bear Market Thinking" Web3 middleware is available, what are we missing from the outbreak?

A chat about recent thoughts on Web3 middleware: data, social graphs, and identity. Web2 giants have long held the ownership and use rights of user data, and there are endless ways to make profits directly or indirectly through data.

2022

What is EpiK Protocol?

In 2012, Google's Knowledge Graph product initially took shape, ushering in the era of knowledge graphs. Up to now, knowledge graphs have been widely used in various tasks of natural language processing, such as information search, automatic question answering, decision analysis, etc. in many fields such as finance, e-commerce, medical care, and government affairs.

2022

AI makes graphics card, graphics card runs AI! Nvidia realizes chip design self-loop?

Bill Dally, chief scientist and senior vice president of research, for example, provides an annual overview of Nvidia's R&D organization and some details on current priorities.

2022

What kind of GPU is the best choice for AI training?

As we all know, today’s state-of-the-art deep learning models take up huge memory space, and many GPUs that used to be powerful in performance may now be slightly insufficient in memory. Explores which GPUs can train models without memory errors, which are better suited for PCs and small workstations. The core conclusion of this article is that video memory size is very important. Yes, video memory size is limiting the training of many deep learning models.

2022

Why GPU can be used to accelerate the computing speed of artificial intelligence or machine learning (parallel computing power)

Calculation is calculation. Mathematically, it is the same. 1+1 is calculated as 2. The CPU can also calculate the neural network. The calculated neural network works well in practical applications, but the speed will be very slow. That's it.

2022

AI chip characteristics and comparison

At present, in the field of intelligent driving, general-purpose chips suitable for parallel computing such as GPUs and FPGAs are mainly used to achieve acceleration in processing deep learning AI algorithms. At the same time, some chip companies have begun to design ASIC-specific chips for AI algorithms, such as Google TPU, Horizon BPU, etc. Before the large-scale rise and mass launch of intelligent driving industry applications, the use of existing general-purpose chips such as GPUs and FPGAs can avoid the high investment and high risk of specializing in the development of custom chips (ASICs). However, because the original intention of such general-purpose chips is not Specifically for deep learning, there are problems such as insufficient performance and high power consumption. These problems will become increasingly prominent as the application scale of the autonomous driving industry expands.

2022

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Shenzhen Lianli Liquid Cooling Technology Co., Ltd

E-mail: sales@lianliai.com

Tel：+86-18002936305

Address: Building 15, No. 6 Baolong Avenue, Longgang District, Shenzhen, Guangdong Province，China

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