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GTC On-Demand

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Abstract:
Automatic speech recognition (ASR) algorithms allow us to interact with devices, appliances, and services using spoken language. Used in cloud services like Siri, Google Voice, and Amazon Echo, speech recognition is growing in popularity, which substantially increases the computational demand on the data center. We'll discuss the latest work by NVIDIA to accelerate the ASR pipeline, which includes a lattice-generating language model decoder, and explain how we're enabling online speech decoding across a range of NVIDIA GPUs.
Automatic speech recognition (ASR) algorithms allow us to interact with devices, appliances, and services using spoken language. Used in cloud services like Siri, Google Voice, and Amazon Echo, speech recognition is growing in popularity, which substantially increases the computational demand on the data center. We'll discuss the latest work by NVIDIA to accelerate the ASR pipeline, which includes a lattice-generating language model decoder, and explain how we're enabling online speech decoding across a range of NVIDIA GPUs.  Back
 
Topics:
Speech and Language Processing, AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9672
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Abstract:
NVIDIA and John Hopkins have partnered up to accelerated speech recognition within the popular Kaldi framework. This framework is the de facto standard when it comes to transcoding recorded audio into the written text. Early results have shown NVIDIA GPUs can provide substantial speedups over pure CPU implementations. This talk will focus on the progress of this effort and the value that GPU acceleration adds to speech recognition.
NVIDIA and John Hopkins have partnered up to accelerated speech recognition within the popular Kaldi framework. This framework is the de facto standard when it comes to transcoding recorded audio into the written text. Early results have shown NVIDIA GPUs can provide substantial speedups over pure CPU implementations. This talk will focus on the progress of this effort and the value that GPU acceleration adds to speech recognition.   Back
 
Topics:
Deep Learning and AI, Accelerated Data Science
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8189
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Abstract:
Voice commands, and advancements in automatic speech recognition algorithms, that help us interact conversationally with devices, appliances and services, are growing within our everyday environment. We will share some highlights and results from work scheduling optimizations in the Kaldi framework. The first part of the talk will describe results focused primarily on optimizing the DNN components of speech pipeline. We will then show results from a GPU optimized fast lattice decode algorithm to achieve high end to end throughput across the whole ASR pipeline from the acoustic model to the language model.
Voice commands, and advancements in automatic speech recognition algorithms, that help us interact conversationally with devices, appliances and services, are growing within our everyday environment. We will share some highlights and results from work scheduling optimizations in the Kaldi framework. The first part of the talk will describe results focused primarily on optimizing the DNN components of speech pipeline. We will then show results from a GPU optimized fast lattice decode algorithm to achieve high end to end throughput across the whole ASR pipeline from the acoustic model to the language model.  Back
 
Topics:
AI Application Deployment and Inference, AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81034
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Abstract:
Maximizing data flow is one of the most important graph problems and has numerous applications across various computational domains: transportation networks, power routing, image segmentation, social network clustering, and recommendation systems. There are many efficient algorithms that have been developed for this problem, most of them trying to minimize computational complexity. However, not all these algorithms map well to massively parallel architectures like GPUs. We'll present a novel GPU-friendly approach based on the MPM algorithm that achieves from 5 to 20 times speedup over the state-of-the-art multithreaded CPU implementation from Galois library on general graphs with various diameters. We'll also discuss some real-world applications of the maximum flow problem in computer vision for image segmentation and in data analytics to find communities in social networks.
Maximizing data flow is one of the most important graph problems and has numerous applications across various computational domains: transportation networks, power routing, image segmentation, social network clustering, and recommendation systems. There are many efficient algorithms that have been developed for this problem, most of them trying to minimize computational complexity. However, not all these algorithms map well to massively parallel architectures like GPUs. We'll present a novel GPU-friendly approach based on the MPM algorithm that achieves from 5 to 20 times speedup over the state-of-the-art multithreaded CPU implementation from Galois library on general graphs with various diameters. We'll also discuss some real-world applications of the maximum flow problem in computer vision for image segmentation and in data analytics to find communities in social networks.  Back
 
Topics:
Accelerated Analytics, Algorithms and Numerical Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7370
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