STRADVISION CTO, Jack Sim, Unveils Innovative Deep Learning Development Methodology at Tech.AD USA 2023

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Dec 11, 2023, 11:00 ET

Efficient Deep Learning: CTO Jack Sim unveils a streamlined development process through teacher-student learning and sprints
Swift Product Releases: Reduces development time by 50%, enabling rapid creation of ImmersiView™ and monthly software releases
Adaptable 3D Perception: The methodology extends to build versatile 3D perception systems, showcasing adaptability to diverse sensor inputs

SEOUL, South Korea, Dec. 11, 2023 /PRNewswire/ -- STRADVISION, an automotive industry pioneer in deep learning-based vision perception technology, announced today that its CTO, Jack Sim, presented a groundbreaking study, "Efficient Deep Learning Development through Teacher-Student Learning and Sprints and Its Application to 3D Perception Systems," at Tech.AD USA 2023. The event, focused on 'level 3 to X autonomy & autonomous vehicle technologies,' took place from December 6 to 8, 2023, at The Henry, Dearborn, Michigan.

STRADVISION CTO, Jack Sim, presented a groundbreaking study, "Efficient Deep Learning Development through Teacher-Student Learning and Sprints and Its Application to 3D Perception Systems," at Tech.AD USA 2023.

In his presentation, Sim unveiled an efficient software development process for multiple deep-learning tasks, leveraging teacher-student learning and sprints. This methodology, concurrently improving accuracy and speed, resulted in a remarkable reduction of product development time by up to 50%.

The process was successfully applied in the creation of STRADVISION's augmented reality product, ImmersiView™. Sim explained that the approach decouples dependencies between tasks, trains individual teacher networks in sprints for improved accuracy, and employs a unified student network for simultaneous task learning.

"To optimize accuracy while maintaining inference speed constraints, STRADVISION applied neural architecture search to the student network in the initial sprints and subsequently fixed the architecture in the remaining sprints," Sim explained. This efficient process enabled the first prototype creation within three months, followed by consistent monthly software releases.

The methodology was further extended to address the challenges of building multi-modal 3D perception systems, demonstrating its adaptability and effectiveness in handling diverse sensor inputs during training. Sim emphasized, "This approach ensures comprehensive and accurate perception, exemplifying the adaptability and effectiveness of our development process in handling diverse sensor inputs in training."

Jack Sim concluded, "Our research underscores the significance of our method in advancing the field of multi-modal perception in autonomous systems, paving the way for the future of automotive technology."

STRADVISION continues to lead the way in deep learning-based vision perception technology, driving innovation and setting new standards for efficient and adaptive development processes.

About STRADVISION

Founded in 2014, STRADVISION is an automotive industry pioneer in artificial intelligence-based vision perception technology for ADAS. The company is accelerating the advent of fully autonomous vehicles by making ADAS features available at a fraction of the market cost compared with competitors. STRADVISION's SVNet is being deployed on various vehicle models in partnership with OEMs; can power ADAS and autonomous vehicles worldwide; and is serviced by over 300 employees in Seoul, San Jose, Detroit, Tokyo, Shanghai, and Dusseldorf. STRADVISION has been honored with Frost & Sullivan's 2022 Global Technology Innovation Leadership Award, the Gold Award at the 2022 and 2021 AutoSens Awards for Best-in-Class Software for Perception Systems, and the 2020 Autonomous Vehicle Technology ACES Award in Autonomy (software category). In addition, STRADVISION and its software have achieved TISAX's AL3 standard for information security management, as well as being certified to the ISO 9001:2015 for Quality Management Systems and ISO 26262 for Automotive Functional Safety.

SOURCE StradVision