Huawei officially launched its Ascend 910 AI processor as well as its "MindSpore" AI framework.
The Ascend 910, which is designed for AI model training, delivers 256 TeraFLOPS for half-precision floating point (FP16), and 512 TeraOPS for integer precision calculations (INT8). Its max power consumption is only 310W. All of these are new industry benchmarks, according to the company.
Huawei claims its MindSpore AI framework is adaptable to all devices, edge, and cloud environments. It helps ensure user privacy because it only deals with gradient and model information that has already been processed. It doesn't process the data itself, so private user data can be effectively protected even in cross-scenario environments.
"We have been making steady progress since we announced our AI strategy in October last year," said Eric Xu, Huawei's Rotating Chairman. "Everything is moving forward according to plan, from R&D to product launch. We promised a full-stack, all-scenario AI portfolio. And today we delivered, with the release of Ascend 910 and MindSpore. This also marks a new stage in Huawei's AI strategy."
Xu also outlined ten areas where Huawei wants to drive change for AI:
- Provide stronger computing power to increase the speed of complex model training from days and months to minutes – even seconds.
- Provide more affordable and abundant computing power. Right now, computing power is both costly and scarce, which limits AI development.
- Offer an all-scenario AI portfolio, meeting the different needs of businesses while ensuring that user privacy is well protected. This portfolio will allow AI to be deployed in any scenario, not just public cloud.
- Invest in basic AI algorithms. Algorithms of the future should be data-efficient, meaning they can deliver the same results with less data. They should also be energy-efficient, producing the same results with less computing power and less energy.
- Use MindSpore and ModelArts to help automate AI development, reducing reliance on human effort.
- Continue to improve model algorithms to produce industrial-grade AI that performs well in the real world, not just in tests.
- Develop a real-time, closed-loop system for model updates, making sure that enterprise AI applications continue to operate in their most optimal state.
- Maximize the value of AI by driving synergy with other technologies like cloud, IoT, edge computing, blockchain, big data, and databases.
- With a one-stop development platform of the full-stack AI portfolio, help AI become a basic skill for all application developers and ICT workers. Today only highly-skilled experts can work with AI.
- Invest more in an open AI ecosystem and build the next generation of AI talent to meet the growing demand for people with AI capabilities.
