StradVision, whose AI-based camera perception software is a leading innovator in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AVs), will reveal insights into optimizing Deep Neural Networks (DNNs) for multiple processors at the 2020 Embedded Vision Summit. During the “Designing Bespoke DNNs for Target Hardware” session, which will be held at 9:30am Pacific Time on September 17, R&D Director and Co-founder Woonhyun Nam will also elaborate on StradVision’s patent know-how via its deep learning-based SVNet technology.

The presentation marks the sixth time that StradVision has participated in the pre-eminent conference and expo devoted to practical, deployed computer vision and visual AI.

This year’s conference includes speakers from Google, Intel, Samsung, Qualcomm, and LG Electronics, with a keynote speech from Google’s Distinguished Engineer David Patterson, who is also a professor at the University of California, Berkeley, and the Vice-Chair of the RISC-V Foundation.

During his session, Nam will touch on StradVision’s established patents, including their patented DNN-enabled SVNet software, as well as discuss the challenges and opportunities of Deep Neural Networks, including cost-effective techniques to optimize DNNs to better fit different processors while also reducing model size and power consumption. The effort required to transform the DNN for each processor can be prohibitive for DNN developers, and Nam will explain how quantization and structured sparsification techniques can help by reducing model size and computation significantly.

A Ph.D. holder of Pohang University of Science and Technology’s Computer Vision Lab, Nam will draw from his experience as the head of the algorithm engineering team at StradVision, whose pioneering SVNet software can quickly and accurately identify potentially hazardous objects and road conditions for ADAS assisted and autonomous vehicles, such as other vehicles, lanes, pedestrians, animals, free space, traffic signs, and lights, even in harsh weather conditions or poor lighting.

Nam’s team is responsible for defining SVNet’s technical architecture, deploying new algorithm pipelines, and delivering algorithm solutions best optimized for each hardware target.

SVNet relies on deep learning-based embedded perception algorithms, which compared with its competitors is more compact and requires dramatically less memory and electricity to run. It supports more than 14 hardware platforms thanks to StradVision’s patented and cutting-edge DNN-enabled software.

Attendees will also learn about StradVision’s experience in bringing AI-based camera perception software to the mass market through international collaboration as well as the view from Asia on the future of the ADAS and AV market.

StradVision’s software is currently being deployed in 8.8 million vehicles worldwide, such as SUVs, sedans, trucks, and self-driving buses, and maintains partnerships with leading global automotive Tier 1 suppliers and five of the world’s top auto OEMs. StradVision’s global partners also include NVIDIA, Aisin Group, Hyundai Motor Group, LG Electronics, Texas Instruments, Renesas, Qualcomm, Xilinx, Socionext, Ambarella, and BlackBerry QNX.

StradVision has obtained China’s Guobiao certification and the coveted ASPICE CL2 (Automotive Software Performance Improvement and Capability Determination Containment Level 2) certification and was recently awarded the Grand Prize in the Electric/Electronic Category at the 14th Korea Patent Excellence Awards.

Since 2012, the Embedded Vision Summit has been organized each May in California by Edge AI and Vision Alliance, an industry partnership operated by Berkeley Design Technology, Inc. Participants regularly include industry experts, business leaders, leading academics, investors, and entrepreneurs interested in visual AI.

Due to global health concerns, the 2020 Embedded Vision Summit will be held online from September 15 to 25.

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StradVision Inc. last week said it had raised $27 million in Series B funding. The Seoul, South Korea-based company makes vision processing technology for autonomous vehicles and advanced driver assistance systems, or ADAS.

Posco Capital led the round, which included investment from IDG Capital, Industrial Bank of Korea, Lighthouse Combined Investment, LSS Private Equity, Mirae Asset Venture Investment, Neoplux, and Timefolio Asset Management. StradVision has raised a total of $40 million to date.

“StradVision’s software solutions for autonomous vehicles and ADAS are proving successful and attractive to leading automakers and suppliers, as our latest round of funding strongly confirms,” stated Junhwan Kim, CEO of StradVision. “We appreciate all of our new investors coming on board, and StradVision will use this funding to take our groundbreaking products to the next level as we lead the advancement of camera technology in autonomous vehicles.”

SVNet provides high-level perception for ADAS

StradVision said it is an industry leader in camera perception software, which plays a critical role in ADAS capabilities such as automatic emergency braking and blindspot detection. The company has registered 75 patents relating to its core technologies, and 79 more patent applications are in process.

The SVNet deep learning-based software enables high-level perception abilities including lane detection, traffic light and sign detection and recognition, object detection, and free space detection, said the company, which also has offices in Tokyo and San Jose, Calif.

The software, which includes SVNet External, SVNet Internal (for monitoring the driver and in-cabin experience), and SVNet Tools, provides real-time feedback, detects obstacles in blind spots, and alerts drivers to potential accidents. SVNet also prevents collisions by detecting lanes, abrupt lane changes, and vehicle speeds, even in poor lighting and weather conditions, according to StradVision.

SVNet’s Auto Labeling System (ALS) produces training data with minimal human input, and a semi-supervised, learning-based SVNet training tool enables customers to enhance SVNet by themselves during mass-production projects. This application of unsupervised learning, which functions similar to how the human brain organizes data, gives machines nearly limitless understanding of the visual information they see.

When paired with commercial, automotive-grade systems on chips (SoCs), SVNet’s deep neural network is fully optimized and interacts in real-time with the world it is viewing. Offering minimum latency and power consumption, the system uses artificial intelligence for proper real-world detection, tracking, segmentation, and classification, the company said.

StradVision working on Level 2 through 4 self-driving cars

The company added that it has partnerships with leading automotive OEMs and Tier 1 suppliers for multiple mass-production projects in China and Europe. Millions of vehicles — including SUVs, sedans, and buses — will be using the SVNet software by 2021, claimed StradVision.

The vision provider recently earned the Automotive SPICE CL2 certification, as well as China’s Guobiao (GB) certificate, and it is already deploying ADAS vehicles on Chinese roads.

StradVision currently has more than 105 employees in the U.S., Germany, South Korea, and Japan. It recently partnered with a leading global Tier 1 supplier and a number of commercial vehicle manufacturers on a side-camera project and custom camera technology for autonomous buses. Current StradVision projects range from Autonomy Levels 2 through 4.

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