The Right Mix of SWaP and AI Performance Previewed in 60 Seconds

ADLINK platforms let you optimize around AI performance, budget, power consumption, and space limit constraints at the edge.

Artificial intelligence (AI) adoption is surging in many embedded market segments, including smart city, healthcare, manufacturing, retail, logistics, and transportation. When deploying AI, OEMs, ODMs, and systems integrators want to optimize cost and performance; use the right I/O interfaces for specific applications; meet size, weight, and power (SWaP) constraints; and build in reliability. Designed to satisfy these challenges, ADLINK Deep Learning Acceleration Platforms (DLAP), combined with the NVIDIA® Jetson™ family, simplify AI deployment on both hardware and software levels to maximize your return on investment.

Drive AI Innovation at the Edge – Deep Learning Acceleration Platforms

Accelerate AI at the Edge - Deep Learning Acceleration Platforms with NVIDIA Jetson Family

DLAP-211 – Edge AI and Intelligent Video Analytics in the Palm of Your Hand

DLAP-301 – AI Vision for Enhanced Safety and Efficiency

DLAP-401 – Deep Learning Acceleration for Autonomous Machines at the Edge (Coming Soon)

Doubts and Challenges When Developing Edge AI Applications

In order to attain an optimized computing platform for deep learning and AI solutions at the edge, developers may need to evaluate many areas. Common doubts and challenges include:

What hardware combination suits my AI solution best?
How my system performs with different neural networks?
Can the hardware meet my requirements?
Benchmark results are not consistent.
Benchmark criteria are different or unknown.
Benchmark results may be out of date.
New or customized neural networks are not benchmarked.

Four Key Considerations for Selecting An Edge AI Solution

Companies seeking to maximize the innovation and productivity gains from deep learning and AI should consider using a computing platform optimized for the associated algorithms. Determine which computing cores are best suited to run the required AI algorithms and how much computing power and I/O bandwidth are needed. Hardware selection should also consider size, weight, power consumption (e.g., SWaP), and cost constraints, particularly when deploying AI at the edge of the network.

AI Workloads

Future-Proof System Design

Reliable System Design

SWaP Optimization

Use Case - Overcome Complex Production Intralogistics of Build-to-Order Manufacturing


Key to the success of the BMW Group is letting their customers “decide for themselves what they want and desire.” This is manifest in giving customers the power to choose between an average of 100 different options across the 40 car models BMW produces. Seeking to increase efficiency in logistics, automotive manufacturers are developing and deploying autonomous mobile robots (AMRs) capable of handling and transporting production material without human intervention. idealworks, a wholly-owned subsidiary of BMW Group, turned to NVIDIA and ADLINK for support in developing the necessary software stacks and robust, edge AI computing platforms. With the implementation of idealworks’ autonomous robots, employees are no longer needed to perform repetitive loading tasks, allowing them to focus on their core competencies.

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Resource Library

User Case
Improving Production Processes with Autonomous Mobile Robots
User Case
Artificial Intelligence and Computer Vision Speeds Canteen/Restaurant Checkout
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Solution Brief
Deep Learning Acceleration Platforms Deliver the Optimal Mix of SWaP and AI Performance
Solution Brief
Making Better Decisions on Embedded Devices with Edge Video Analysis (EVA)
Brochure
Deep Learning Acceleration Plat-forms Deliver the Optimal Mix of SWaP and AI

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