Kneron acquires Otus to advance edge AI for the autonomous vehicle industry

Kneron acquires Otus to advance edge AI for the autonomous vehicle industry

Kneron, an edge AI hardware solutions company, has announced the acquisition of Otus, an imaging technology provider. Both companies previously worked together to develop full-stack solutions for incorporating software and hardware in autonomous vehicles.

Kneron thinks that acquiring this company will enhance its market position in the automotive industries of Japan and the US, where there is a high demand for imaging solutions that incorporate artificial intelligence. Otus provides imaging solutions to Japanese and US customers and has established itself as a preferred partner of several Japanese vehicle manufacturers.

Otus developed systems-on-chip with advanced imaging capabilities to serve the growing autonomous vehicle market. However, these chipsets lacked artificial intelligence capabilities.

Otus CEO Alex Chiu says, “By integrating Kneron’s edge AI capabilities with Otus’ advanced imaging solutions, we are able to create more complete product lines that will better serve our automotive customers.”

Kneron recently announced that its KL720 power-efficient AI chips would support the Qualcomm Robotics RB1 and RB2 platforms for industrial applications with automatic optical inspection, safety monitoring and predictive maintenance use cases.

Dev Singh, Qualcomm Technologies VP of business development, adds, “We are pleased to work with Kneron. With the KL720 supporting our robotics and IoT platforms, we amplify the value of the combined solutions and better serve customer applications.”

The KL720 is a system-on-chip for edge AI that merges the Arm Cortex-M4 processing unit with the power to manage 4K images specifically for the automotive industry. Kneron created this SoC for AIoT and advanced IP camera functions.

In 2018, Kneron was recognized as a Cool Vendor in the edge computing space by Gartner. According to Gartner’s report, Kneron can effectively tackle organizations’ challenges when implementing edge-based architectures.

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