How to Accelerate AI at the Edge
AI and ML models are moving to the edge for inferencing. If the public cloud is the preferred environment for training the models, it is the edge where most of the models are deployed for inferencing. Unlike the cloud and data center, edge computing environments are highly constrained. They are deployed in remote, harsh environments that make them difficult to manage. To optimize deep learning models for the edge, both hardware and software vendors have built various platforms to accelerate the inferencing. This session introduces the current state of [email protected] while discussing various choices from Amazon, Google, Intel, Microsoft, NVIDIA, and Qualcomm.
Outline/Structure of the Demonstration
- Why edge has become THE destination for AI
- Building blocks of an edge computing platform
- Hardware AI Accelerators
- Software for optimizing and deploying the models at the edge
- Tradeoffs and recommendations
Learning Outcome
- State of edge computing for AI
- Best practices of optimizing and deploying models at the edge
- Vendor strategies
Target Audience
AI Engineers, Data Scientists, Software Developers
Links
- Keynote - Digital Transformation at DigitalOcean TIDE Conference
- Forbes Article - How AI Accelerators Are Changing The Face Of Edge Computing
- Forbes Article - Demystifying Edge Computing -- Device Edge vs. Cloud Edge
- Forbes Article - How Edge Computing And Serverless Deliver Scalable Machine Learning Services
- Interview - Evolution of Edge Computing
schedule Submitted 3 years ago
Recent Activities
- comment Kuldeep Jiwani commented "Hi Janakiram, Have you im..." on proposal How to Accelerate AI at t...schedule 2 years ago
- reply Janakiram MSV replied "Hi Sujoy - It will have a..." on the comment please suggest what of th...schedule 2 years ago
- comment Sujoy Roychowdhury commented "please suggest what of th..." on proposal How to Accelerate AI at t...schedule 2 years ago
Call for Proposals CLOSED
Ended on Apr 1 '20 11:59 PM IST
We are looking for deep technical topics related to Data Science. We are committed to diversity and believe in transparency. Hence, all proposals will be public.
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Natasha Rodrigues
Deepti Tomar
Kuldeep Jiwani
Ashay Tamhane
Sujoy Roychowdhury
