Designed to accelerate compute-intensive AI inferencing and learning tasks, the DLAP x86 series optimizes SWaP and AI performance at the edge, offering high performance per watt and per dollar
ADLINK Technology Inc., a global leader in edge computing has launched one of the most compact GPU-enabled deep learning acceleration platforms on the market with its latest DLAP x86 series. The DLAP x86 series targets the deployment of deep learning in volume, at the edge where data is generated and actions are taken. It is optimized to deliver AI performance in various industry applications by accelerating compute-intensive, memory-hungry AI inferencing and learning tasks.
“Large multilayered networks? Complex datasets? This is what the DLAP x86 was designed for. The value of ADLINK’s DLAP series is the flexibility it provides for deep learning applications; architects can choose the optimal combination of CPU and GPU processors based on the demands of an application’s neural networks and AI inferencing speed, yielding a high performance per dollar,” said Zane Tsai, Director of ADLINK Embedded Platforms & Modules Product Center.
The DLAP x86 series features:
• Heterogeneous architecture for high performance – featuring Intel® processors and NVIDIA Turing™ GPU architecture delivering higher GPU-accelerated computation than others and returning optimized performance per watt and per dollar.
• The DLAP x86 series’ compact size starts at 3.2 liters; it is optimal within mobility devices or instruments where physical space is limited, such as mobile medical imaging equipment.
• With a rugged design for reliability, the DLAP x86 series can sustain temperatures up to 50 degrees Celsius/240 watts of heat dissipation, strong vibration (up to 2 Grms) and shock protection (up to 30 Grms), for reliability in industrial, manufacturing and healthcare environments.
Delivering an optimal mix of SWaP and AI performance in edge AI applications, the DLAP x86 helps transform operations in healthcare, manufacturing, transportation and other sectors. Examples of use include:
• Mobile medical imaging equipment: C-arm, endoscopy systems, surgical navigation systems
• Manufacturing operations: object recognition, robotic pick and place, quality inspection
• Edge AI servers for knowledge transfer: combining pre-trained AI models with local data sets
