At Rise of AI Summit 2020 in Berlin, Evgeniy Vlasov, senior alliance and business development manager at Huawei Technologies, explains why AI processing is moving from data centers to the edge, close to where data is generated. He argues that classical CPUs are not built for AI workloads and that over 90 percent of edge AI use cases today are inference rather than training, so edge devices need dedicated chipsets and must balance computing power against energy consumption, especially for battery-powered robots and drones. He presents Huawei's Atlas edge AI stack, including the Atlas 200 module, Atlas 500 edge station and Atlas 300 inference card, built on Huawei's own chipsets and the open-source MindSpore framework, which also supports third-party hardware and frameworks such as TensorFlow and PyTorch. He then walks through concrete deployments: autonomous delivery vehicles from partner Neolix, computer-vision quality control in manufacturing, and fleet management for automated guided vehicles with partner Gestalt Robotics, arguing edge devices work alongside the cloud rather than replacing it.
Key takeaways
- Over 90 percent of current edge AI use cases are inference rather than training, according to Vlasov.
- Edge devices are meant to work together with the cloud, not replace it, for storing data and retraining models.
- Huawei's MindSpore framework is open source and works with third-party hardware like NVIDIA and frameworks like TensorFlow and PyTorch.
- AI-based computer vision quality control can raise manufacturing defect detection from under 90 percent to around 99 percent.
- Running AI processing on a dedicated edge server instead of onboard the robot extends AGV battery life by about 8 to 25 percent.
Detailed notes
Why AI is moving to the edge
Vlasov describes how AI long stayed in data centers with sufficient compute power, but as software and hardware improved and became more affordable, AI began handling specialized tasks with lower resource needs. He attributes the shift to the edge to the rise of consumer devices (smartphones, smartwatches, smart home systems) that need AI capability without relying only on the cloud, improved wireless and fixed connectivity, and, in industry, a demand for the next level of process automation in manufacturing, autonomous driving, healthcare and smart cities. He defines edge AI as algorithms or applications processed locally on hardware close to the data source, avoiding the need to stream or store large volumes of data in the cloud, which matters for use cases needing high-speed processing with minimum latency, such as factory robots or drones. Edge does not require building an AI module into the device itself; it can also be an AI server in a factory network or an edge station near the data source.
Requirements for edge AI deployment
He notes AI computing needs dedicated chipsets, since classical CPUs can run AI algorithms only slowly. Of the two main milestones, training and inference, over 90 percent of edge AI cases are inference. Edge devices must balance computing power against energy consumption because many, such as drones, electric vehicles and robots, run on batteries, which limits how many AI scenarios they can run simultaneously without losing speed or efficiency. Edge and cloud work together rather than the edge replacing the cloud: data can be stored, analyzed and used to retrain models, which are then redeployed to multiple devices. He stresses the importance of a universal platform that lets developers build an AI application once and deploy it across different device topologies and scenarios, plus industry-specific SDKs for domains like robotics, manufacturing and computer vision to simplify development. Managing large fleets of AI-powered devices, for example in traffic monitoring or video surveillance, requires systems for device management and maintenance, data analytics, model retraining and redeployment.
Huawei's Atlas edge AI stack
Vlasov presents Huawei's Atlas AI computing solution, described as "all scenario, full stack": it covers public and private cloud, edge computing and industry-specific deployment scenarios, and spans the full stack from AI acceleration hardware based on Huawei's own Ascend chipsets through to application development tools. The stack includes Huawei's own MindSpore framework, which is open source on GitHub and, while originally built for Huawei's own chipsets, also works with third-party AI acceleration hardware such as NVIDIA's, and developers can alternatively use frameworks like TensorFlow, Caffe or PyTorch. On the hardware side he highlights the Atlas 200 AI module, roughly the size of half a credit card and consuming up to 8 watts, which can be integrated into cameras, drones or robots; the Atlas 500 edge station, a compact standalone device with Wi-Fi, LTE and Ethernet connectivity and passive cooling for harsh environments; and the Atlas 300 inference card, a PCI card based on four Ascend chips for installation in third-party servers.
Industry use cases
He describes Neolix, a strategic partner developing self-driving delivery vehicles used in hospitals, gated areas, parks and Winter Olympic facilities, with a modular design separating the autonomous driving platform from a customizable top layer for retail delivery, logistics or security patrol. Neolix vehicles use the Atlas 500 edge station for people and object detection, motion analysis and gesture recognition, such as stopping when a person waves. In manufacturing, edge servers run computer vision applications for real-time quality control, visual inspection and defect detection; Vlasov states traditional machine vision achieves under 90 percent detection accuracy, which AI-based computer vision can raise to around 99 percent while also increasing processing speed. For automated guided vehicle (AGV) fleets, built with partner Gestalt Robotics, he contrasts embedding AI acceleration directly into each robot with placing an edge server on the factory network to manage a mixed fleet, arguing the latter is more universal, modular in functionality and cost, and saves battery power, extending robot operating time between charges by roughly 8 to 25 percent depending on the robot.
Q&A highlights
Asked whether the Neolix vehicles are already deployed, Vlasov confirms the company has been running fleets for several years, though deployment is still limited by driving regulations in different regions. Asked who the main customers are, he says personally "everybody who is really interested in AI," but notes that in practice it depends on the country: in France and Germany manufacturing dominates demand, while in Spain and Italy the focus leans toward retail or medical applications, with the pandemic increasing partner work on AI in the medical sector. Asked about the roadmap, he points to further push toward edge-to-cloud collaboration where applicable, making devices more affordable in software and deployment, and more work on training rather than only inference, including developments related to video quality.
Quotes
For a long time, artificial intelligence stayed in data centers where there was sufficient computing power to perform processor demanding tasks.
Evgeniy Vlasov
My personal answer is everybody who are really interested in AI.
Evgeniy Vlasov
Questions answered in this talk
What is edge AI computing?
According to Evgeniy Vlasov at Rise of AI Summit 2020, edge AI refers to algorithms or applications processed locally on hardware located close to the data source, so data does not need to be streamed or stored in the cloud for analysis.
What hardware does Huawei offer for edge AI?
According to Vlasov, Huawei's Atlas line includes the Atlas 200 AI module (about half a credit card in size, up to 8 watts), the Atlas 500 edge station with Wi-Fi, LTE and Ethernet connectivity for harsh environments, and the Atlas 300 PCI inference card based on four Ascend chips.
How much can edge AI improve manufacturing quality control?
Vlasov states that traditional machine vision achieves under 90 percent defect detection accuracy, while AI-based computer vision algorithms can raise this to around 99 percent and increase processing speed.