China shows that even with a low-cost AI chip it can develop hypersonic flight
A team of Chinese scientists has published a step-by-step guide for integrating a low-cost and readily available artificial intelligence (AI) chip module to boost the performance of hypersonic vehicles.
The chosen chip, NVIDIA Jetson TX2i, has been shown to significantly increase scramjet engine control, autonomy and flight stability in hypersonic situations.
Additionally, the chip is relatively cheap, making it a strong consideration for anyone building hypersonic vehicles. In this case, the team used the chip on an air-powered vehicle capable of achieving flight speeds of more than Mach 7.
According to the research team, this module can efficiently process computational fluid dynamics models. This means that calculations that used to take seconds to complete can now be done in just 25 milliseconds, four times faster than the blink of an eye.
The research team from the Beijing Electric Machine Research Institute and Dalian University of Technology published its findings in a peer-reviewed paper on March 13 in the Chinese academic journal Propulsion Technology.
The team found that the module's response speed was ideal for real-time fuel system optimization, fault diagnosis and fault-tolerant control in scramjet engines.
The team found that the module's peak single-precision performance was approximately 1.26 teraflops ( TFLOPS ). That's only about 2% of the capability of the company's most powerful AI chip, the H100.
In case you don't know, a teraflop is a measure of a GPU's performance and is often critical when comparing graphics cards. To put this chip's TFLOPS performance into perspective, an Xbox Series
There's one problem, though: the H100 is expensive, priced in the tens of thousands of dollars. On the other hand, Jetson TX2i is relatively cheaper and can be purchased for a few hundred euros, no more than 500, so we can talk about a proletarian hypersonic flight. Additionally, US export controls do not restrict it, so it can be purchased online.
Nvidia H100
According to the team led by Professor Sun Ximing, the Jetson TX2i module in the scramjet engine control system has increased the range and stability of hypersonic vehicles, while reducing research and development costs.
“High-performance graphics cards possess excellent computing capabilities, but require supporting equipment such as a hosting platform, power supply and radiator,” the Sun team explained.
“They have disadvantages such as high power consumption, high weight and large size, which do not meet the demands of lightweight and small-sized embedded controllers in the aerospace industry,” the Sun team wrote in its paper.
Why did the team publish this guide?
The document provides detailed guidance with formulas and addresses potential engineering challenges, such as limiting simulation grid size, memory management, code optimization, and specific compilation instruction schemes.
The team said in the paper: “To apply [the AI chip] to hypersonic vehicles, further work in inlet modeling, shock wave correction and data modeling is needed.”
According to the South China Morning Post (SCMP), it is unclear why the researchers chose the Nvidia chip for their experiment. However, SCMP notes that China is unlikely to use this chip to power its hypersonic vehicles in the future.
It is not possible that the Chinese technicians are making fun of the American ones who have such great difficulties in developing a hypersonic vehicle, telling them that, even with a processor that is not exactly cutting edge, they can similar and guide hypersonic flight. Because China certainly has, at its disposal, much better processors than the mediocre ones presented here.
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The article China shows that even with a low-cost AI chip it can develop hypersonic flight comes from Economic Scenarios .
This is a machine translation of a post published on Scenari Economici at the URL https://scenarieconomici.it/la-cina-dimostra-che-perfino-con-un-chip-ai-a-basso-costo-puo-sviluppare-il-volo-ipersonico/ on Thu, 18 Apr 2024 05:10:48 +0000.