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GTC On-Demand

Presentation
Media
Abstract:
Data centers today benefit from highly optimized hardware architectures and performance metrics that enable efficient provisioning and tuning of compute resources. But these architectures and metrics, honed over decades, are sternly challenged by the rapid increase of AI applications and neural net workloads, where the impact of memory metrics like bandwidth, capacity, and latency on overall performance is not yet well understood. Get the perspectives of AI HW/SW co-design experts from Google, Microsoft, Facebook and Baidu, and technologists from NVIDIA and Samsung, as they evaluate the AI hardware challenges facing data centers and brainstorm current and necessary advances in architectures with particular emphasis on memory's impact on both training and inference.
Data centers today benefit from highly optimized hardware architectures and performance metrics that enable efficient provisioning and tuning of compute resources. But these architectures and metrics, honed over decades, are sternly challenged by the rapid increase of AI applications and neural net workloads, where the impact of memory metrics like bandwidth, capacity, and latency on overall performance is not yet well understood. Get the perspectives of AI HW/SW co-design experts from Google, Microsoft, Facebook and Baidu, and technologists from NVIDIA and Samsung, as they evaluate the AI hardware challenges facing data centers and brainstorm current and necessary advances in architectures with particular emphasis on memory's impact on both training and inference.  Back
 
Topics:
Data Center and Cloud Infrastructure, Performance Optimization, Speech and Language Processing, HPC and AI, HPC and Supercomputing
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91018
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Abstract:
Learn why deep learning scales so well and how to apply it to important open problems. Deep learning has enabled rapid progress in diverse problems in vision, speech, and beyond. Driving this progress are breakthroughs in algorithms that can harness massive datasets and powerful compute accelerators like GPUs. We'll combine theoretical and experiment insights to help explain why deep learning scales predictably with bigger datasets and faster computers. We'll also show how some problems are relatively easier than others and how to tell the difference. Learn about examples of open problems that cannot be solved by individual computers, but are within reach of the largest machines in the world. We'll also make the case for optimizing data centers to run AI workloads. Finally, we'll outline a high-level architecture for an AI datacenter, and leave you with powerful tools to reach beyond human accuracy to confront some of the hardest open problems in computing.
Learn why deep learning scales so well and how to apply it to important open problems. Deep learning has enabled rapid progress in diverse problems in vision, speech, and beyond. Driving this progress are breakthroughs in algorithms that can harness massive datasets and powerful compute accelerators like GPUs. We'll combine theoretical and experiment insights to help explain why deep learning scales predictably with bigger datasets and faster computers. We'll also show how some problems are relatively easier than others and how to tell the difference. Learn about examples of open problems that cannot be solved by individual computers, but are within reach of the largest machines in the world. We'll also make the case for optimizing data centers to run AI workloads. Finally, we'll outline a high-level architecture for an AI datacenter, and leave you with powerful tools to reach beyond human accuracy to confront some of the hardest open problems in computing.  Back
 
Topics:
AI and DL Research, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9643
Streaming:
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