Josh Park

Josh Park is an Automotive Solutions Architect Manager at NVIDIA. To date, he has been working on deep learning solutions using DL framework such as TensorFlow on muti-GPUs/multi-nodes servers and embedded systems. Also, he has been evaluating and improving training and inference performances on various GPUs + x86_64/aarch64. He received B.S and M.S. degrees from Korea University, and Ph.D. degree from Texas A&M University in Computer Science.

Posts by Josh Park

AI / Deep Learning

Discovering GPU-friendly Deep Neural Networks with Unified Neural Architecture Search

After the first successes of deep learning, designing neural network architectures with desirable performance criteria for a given task (for example… 9 MIN READ
AI / Deep Learning

Estimating Depth with ONNX Models and Custom Layers Using NVIDIA TensorRT

TensorRT is an SDK for high performance, deep learning inference. It includes a deep learning inference optimizer and a runtime that delivers low latency and… 10 MIN READ
AI / Deep Learning

Speeding Up Deep Learning Inference Using TensorRT

This is an updated version of How to Speed Up Deep Learning Inference Using TensorRT. This version starts from a PyTorch model instead of the ONNX model… 22 MIN READ
AI / Deep Learning

Speeding up Deep Learning Inference Using TensorFlow, ONNX, and TensorRT

Starting with TensorRT 7.0, the Universal Framework Format (UFF) is being deprecated. In this post, you learn how to deploy TensorFlow trained deep learning… 15 MIN READ
Autonomous Vehicles

Object Detection and Lane Segmentation Using Multiple Accelerators with DRIVE AGX

Autonomous vehicles require fast and accurate perception of the surrounding environment in order to accomplish a wide set of tasks concurrently in real time. 17 MIN READ