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Abstract:
Autonomous driving systems use various neural network models that require extremely accurate and efficient computation on GPUs. This session will outline how Zoox employs two strategies to improve inference performance (i.e., latency) of trained neural network models without loss of accuracy: (1) inference with NVIDIA TensorRT, and (2) inference with lower precision (i.e., Fp16 and Int8). We will share our learned lessons about neural network deployment with TensorRT and our current conversion workflow to tackle limitations.
Autonomous driving systems use various neural network models that require extremely accurate and efficient computation on GPUs. This session will outline how Zoox employs two strategies to improve inference performance (i.e., latency) of trained neural network models without loss of accuracy: (1) inference with NVIDIA TensorRT, and (2) inference with lower precision (i.e., Fp16 and Int8). We will share our learned lessons about neural network deployment with TensorRT and our current conversion workflow to tackle limitations.  Back
 
Topics:
Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9895
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