DeepScale, a start-up based in Mountain View, California, announced $15 million in Series A funding for its work in efficient deep learning perception software for use in mass-produced automated vehicles.
DeepScale said it is using efficient deep neural networks (DNNs) on small, low-cost, automotive-grade sensors and processors. The goal is to interpret and classify sensor data in real-time for automated vehicles.
"One of our core objectives is to drastically reduce the number of deaths and injuries on the road," said Forrest Iandola, co-founder and CEO of DeepScale. "The company's Series A funding will not only empower our engineering team to continue to make breakthroughs in automated driving safety, but will also help us attract the brightest talent in the industry to transform the future of transportation."
DeepScale says it has lined up a number of strategic partnerships with Tier 1 suppliers, OEMs and semiconductor suppliers to provide automated driving perception solutions, including Visteon and HELLA-Aglaia Mobile Vision GmbH, a major German automotive supplier.
The funding round was led by Point72 and next47. Additional Series A funding was provided by existing investor Autotech Ventures and Trucks Venture Capital. Previous seed investors included Andy Bechtolsheim, Ali Partovi, Lip-Bu Tan and Jerry Yang's AME Cloud Ventures.
