Accelerate Your Edge AI Deployment

GTC Conference and Training
April 12-16, 2021

Artificial intelligence (AI) is ushering in a new age of computing, enabling greater innovation, productivity, and cost savings. For time- and safety-critical edge applications, like medical imaging, and industrial-grade intelligent video analytics, edge AI solutions can better satisfy stringent response times than cloud AI.

OEMs, ODMs, and systems integrators can learn how to speed up their edge AI development by signing up for ADLINK’s on-demand sessions at NVIDIA GTC, taking place online April 12-16, 2021.

  • Learn how edge AI is advancing medical and industrial applications
  • Gain insights about overcoming edge AI challenges
  • Hear how to drive outcomes with IVA using ADLINK hardware and NVIDIA SDKs


Sign Up: ADLINK On-Demand Sessions

You will be able to reserve your free spot for ADLINK’s on-demand sessions by registering at NVIDIA GTC website, allowing you to sign up for and attend live on-demand sessions across all GTC regions.

[SS32701] How to Improve Speed and Accuracy in Medical Imaging
Zane Tsai, Director of EPM-Platforms Product Center
Medical imaging equipment is used to help doctors investigate signs and symptoms, take biopsies, and treat lesions. To provide better medical care, medical imaging equipment must innovate relentlessly to provide real-time visual guidance for surgeons to operate and reduce the time an image is created to server more patients. Also, the image quality must be enhanced without increasing harmful radiation exposure of a patient to assist doctors to make precise diagnosis. Like it is not challenging enough, the advancement of medical imaging technology is also preferably delivered in a smaller form factor for use in operating rooms or clinics in outlying areas where local doctors can consult peer doctors through telemedicine. Attend the webinar to see how ADLINK can help medical equipment manufactures tackle the challenges to help improve healthcare services.
[SS32770] Driving Operational Efficiency with NVIDIA Transfer Learning Toolkit, Pre-trained Models, and DeepStream SDK
Toby McClean, VP, IoT Technology and Innovation
ADLINK is driving actions and outcomes from machine-vision AI deployments, using NVIDIA Deep Stream, Transfer Learning Toolkit (TLT), and pre-trained models.

Drawing on real-life industrial use cases for quality inspection and intelligent video analytics (IVA), this presentation will describe how to build solutions that are easy to run, scale, and drive action using ADLINK’s approach. In one example, we detect people who are loitering in company spaces using the Deep Stream pipeline that includes the PeopleNet model and sounds an alarm through general-purpose I/O (GPIO).

You will learn how to create solutions to drive actions and outcomes, using ADLINK’s range of hardware solutions combined with NVIDIA’s software development kits (SDKs), including Deep Stream, Transfer Learning Toolkit, and Tensor RT. Application speed, scalability, and simple user experience are fundamental benefits by design.

The session will also address how connecting the results generated by Deep Stream pipelines to other devices is important as developers build solutions to drive outcomes.
[SS32769] Overcome Challenges of Edge AI- Optimize Edge AI for both Performance and SWaP
Olivia Chan, Senior Product Manager, EPM BU
AI applications are gaining traction at the edge, promising performance, production, and efficiency improvements for all walks of life. To ensure the successful implementation of AI applications at the edge, developers face a myriad of difficult choices from hardware, AI network, and framework options that may differ from application to application. Moreover, running AI at the edge, opposed to in the cloud, gives ruggedness a new meaning and presents extra challenges to developers. In this session, ADLINK will address key considerations to developing a fully-fledged AI applications at the edge citing various industrial use cases and share tips to optimize AI implementation from day zero and for post-deployment upgrade.

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