Seoul, South Korea

Taewoong Jang

Average Co-Inventor Count = 12.7

ph-index = 13

Forward Citations = 728(Granted Patents)

Forward Citations (Not Self Cited) = 456(Sep 21, 2024)

DiyaCoin DiyaCoin 0.33 

Inventors with similar research interests:


Location History:

  • Gyeongsangbuk-do, KR (2017)
  • Seoul, KR (2018 - 2022)


Years Active: 2017-2022

where 'Filed Patents' based on already Granted Patents

147 patents (USPTO):

Title: Taewoong Jang: Innovating Hardware Optimization in Neural Networks

Introduction:

In the field of artificial intelligence and deep learning, Taewoong Jang has emerged as a prominent figure with his groundbreaking innovations. Hailing from Seoul, Korea, Jang has achieved remarkable success in the optimization of hardware for Convolutional Neural Networks (CNNs). With an impressive repertoire of 147 patents to his name, Jang's contributions are revolutionizing the way we approach hardware optimization in neural networks.

Latest Patents:

Jang's latest patents are focused on two key areas: learning methods and testing devices for CNNs, and on-device continual learning of neural networks for various applications. His recent patents include:

1. Learning method and learning device for CNN using 1xK or Kx1 convolution to be used for hardware optimization:

This patent presents a methodology for training CNN parameters using 1xK or Kx1 convolution operations, specifically designed for hardware optimization. The method involves reshaping and adjusting feature maps, enhancing the efficiency and meeting Key Performance Indicators (KPIs) for hardware optimization.

2. Method and device for on-device continual learning of a neural network, and method and device for testing the neural network:

In this patent, Jang introduces a technique for continual learning of neural networks on the device itself, applicable to smartphones, drones, vessels, and military purposes. The method involves sampling new data, generating synthetic previous data, and current-learning batches using generative adversarial networks (GANs) and online learning. This approach saves resources, prevents catastrophic forgetting, and ensures privacy.

Career Highlights:

Taewoong Jang's career is testament to his dedication and expertise in the field of hardware optimization for neural networks. He has worked with renowned companies such as StradVision, Inc., a leading provider of AI-based camera perception solutions, and StadVision, Inc.*** (Note: Please provide the correct company name as the given spelling seems incorrect). Through his roles in these organizations, Jang has been at the forefront of developing innovative solutions and patents related to hardware optimization and neural networks.

Collaborations:

Collaboration is crucial in the field of innovation, and Taewoong Jang has been fortunate to work alongside talented individuals, including Hojin Cho and Yongjoong Kim. Together, they have collectively contributed to the advancement of hardware optimization techniques and on-device continual learning methodologies.

Conclusion:

Taewoong Jang's relentless pursuit of hardware optimization for neural networks has brought about significant advancements in the field of artificial intelligence. With an impressive patent portfolio and collaborations with industry experts, Jang's innovations are shaping the future of CNNs and their applications in various domains. His contribution in on-device continual learning and hardware optimization paves the way for more efficient, privacy-enhanced, and resource-saving neural network models. As the world embraces AI, innovators like Jang play an indispensable role in pushing the boundaries of what is possible.

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