Communities across the globe are grappling with the consequences of climate change, ranging from devastating floods to wildfires. As a result of abnormal climate patterns, forests are becoming increasingly parched, and heat waves have begun to emerge, exacerbating the destructive nature of wildfires. To effectively address this challenge, we explore innovative solutions to help communities prepare for and respond to climate-related disasters and threats.
Early detection and monitoring are crucial strategies for minimizing wildfire damage and have garnered significant research attention in artificial intelligence and computer vision. Innovative AI models, relying on satellite imagery, have been developed to pinpoint wildfire boundaries and display their real-time locations. With the rapid advancement of digital cameras and image processing technologies, deep learning object detection algorithms, utilizing parallel computing and graphics cards, can achieve real-time or near-real-time processing speeds and enhance detection accuracy.
A leading software company collaborated with Lanner to design a jointly-developed Edge AI Server for advanced wildfire monitoring system, which required the following features.
- GPU Acceleration.
Deep learning models benefit significantly from GPU acceleration. The appliance must be compatible with powerful GPUs that can handle the training and inference tasks efficiently.
- Processing Power.
A high-performance multi-core CPU is essential to support overall system operation and orchestration tasks, enabling workloads like AI and Deep Learning applications.
- Abundant I/O Network Interface.
A rich series of input/output ports and extensive LAN port configurations, including GbE LAN, SFP+ fiber, and IPMI connectivity to communicate with systems and sensors for remote monitoring.
Expansion features are needed for better performance even as demands increase, including additional storage, and PCIe slots for graphics cards, or accelerator cards.
- High Availability.
High availability design involves implementing powers and swappable fans to ensure that the edge server remains operational and accessible, even in the face of hardware failures
Advanced wildfire monitoring system would leverage Edge AI Server to monitor, analyze, predict, and communicate information related to wildfires and their connection to climate change. It would enhance our ability to respond to these increasingly frequent and severe natural disasters.
Lanner’s NCA-6530 is a high-performance 2U 19” rackmount Edge AI Server with support for dual 4th Gen Intel Xeon Scalable processors (codenamed Sapphire Rapids-SP) and features a built-in accelerator for high throughputs, advanced hardware-enabled security, and support for up to 8x NIC modules for expansion.
The NCA-6530’s compatibility with NVIDIA A30 Tensor Core GPU makes available versatile and unprecedented compute acceleration for mainstream enterprise servers at every scale, not only providing the necessary computational power for real-time AI inference but also enabling accelerated deep learning algorithms for security tasks including data mining, data collation and sifting. This appliance supports up to 1536GB of system memory, 6x hot-swappable fans, 1600W/2000W redundant power supply, Intel® QAT, and optional PCIe expansion.
2U 19" Rackmount Network Appliance Built with Intel® Xeon® Processor Scalable Family (Codenamed Ice Lake SP)
|CPU||Intel® Xeon® Processor Scalable Family(Codenamed Ice Lake SP)|
2U 19” Rackmount Network Appliance Built With Intel® Xeon® Processor Scalable Family (Codenamed Sapphire Rapids-SP)
|CPU||Intel® Xeon® Processor Scalable Family (Sapphire Rapids-SP)|