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GTC ON-DEMAND

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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 & Cloud Infrastructure, Performance Optimization, Speech & Language Processing, HPC and AI, HPC and Supercomputing
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91018
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