Empowering Edge AI Applications with Power-Efficient GPU Solutions - Lanner Electronics | Network Appliance | uCPE SD-WAN| MEC Server | Intelligent Edge Appliance

The relentless advance of artificial intelligence (AI) in edge networks — from retail, manufacturing to smart cities —has ignited a parallel evolution in the hardware that powers it. Central to this hardware revolution is the Graphics Processing Unit (GPU), a piece of technology initially designed for rendering images but now indispensable to processing the complex algorithms that drive Edge AI applications.

Requirement for GPUs Deployed in Edge Networks

Edge AI's requirement for computation and power efficiency has transformed the GPU landscape. This shift has not only changed the trajectory of GPU development but has also set new benchmarks for what these processors are expected to deliver.

• Improved Power Efficiency:
  GPUs must be highly power-efficient to operate in environments where energy resources are limited or expensive.
   
• High Performance:
  Despite the small size and lower power consumption, these GPUs need to offer significant computational power to handle real-time data processing and AI tasks.
   
• Robustness and Reliability:
  GPUs used in edge environments must be durable and able to operate reliably in a wide range of environmental conditions, including extremes of temperature, humidity, and vibration.
   
• Security Features:
  Given the distributed nature of edge computing, GPUs must include advanced security features to protect sensitive data against unauthorized access and cyber threats.

SiMa.ai ML SoC and Palette™ Software Platform

Startups such as SiMa.ai are leading the way in GPU technology, notably improving power efficiency and setting new industry standards. SiMa.ai's Machine Learning System-on-Chip (SoC) architecture, meticulously designed from the ground up, maximizes efficiency for AI workloads. This design achieves higher throughput on AI inference tasks while consuming a fraction of the power compared to traditional GPUs. The power-efficient processing capabilities of SiMa.ai's technology mean that AI can be deployed more widely, ranging from edge computing applications to the powering of the next generation of smart devices, all while minimizing environmental impact.

 

SiMa.ai’s Palette™ software platform is tailored for comprehensive ML stack application development, accommodating any ML workflow intended for deployment on the edge while maintaining optimal performance and user-friendliness.

With Palette Edgematic, users can swiftly prototype computer vision pipelines using a graphical drag-and-drop interface, streamlining the process to mere minutes. Both Palette tools employ a pushbutton build approach, eliminating the necessity for manual low-level coding in edge ML solutions.

Bundled Edge AI Solutions

Lanner collaborates with SiMa.ai to build the Edge Platform, featuring Lanner Edge AI appliances LEC-2290 and EAI-I730, SiMa.ai’s Machine Learning System on a Chip (MLSoC), and Palette™ Edgematic Software Platform.

With push button performance, we enable the most efficient Edge Machine Learning Platform by connecting a higher number of cameras per appliance, processing videos with a higher frame rate per second, and receiving higher resolution camera data

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