New SiMa.ai design centre in Bengaluru

By: |
February 1, 2021 3:45 AM

Investment in the new site, the company said, will bolster its engineering and operations team, and open up new in-region job opportunities for board development, operations, infrastructure, and system application roles.

SiMa.ai’s MLSoC platform environment enables widespread adoption of ML at the embedded edge by supporting any model, any neural network, any framework for any workload.SiMa.ai’s MLSoC platform environment enables widespread adoption of ML at the embedded edge by supporting any model, any neural network, any framework for any workload.

SiMa.ai, the machine learning company, has opened a new design centre in Bengaluru. Investment in the new site, the company said, will bolster its engineering and operations team, and open up new in-region job opportunities for board development, operations, infrastructure, and system application roles. “Growth beyond the Silicon Valley and Serbia will give SiMa.ai a solid foundation with engineering talent in India to jump-start the productisation of SiMa.ai’s MLSoC platform, while scaling the team for next-generation product development,” the company said in a statement.

Krishna Rangasayee, founder and CEO of SiMa.ai, said, “The embedded edge market is ripe for disruption and machine learning has moved from nice-to-have to must-have. Expansion into India is pivotal as we prepare to deliver the industry’s first purpose-built machine learning SoC.”

SiMa.ai’s engineering team in India will report to Gopal Hegde, senior vice-president, Engineering and Operations. Hegde stated, “We go where outstanding engineers are located. Bengaluru has a talent pool of highly qualified and experienced engineers and our expansion into the region is a strategic move as we grow our engineering organisation.”

SiMa.ai’s MLSoC platform environment enables widespread adoption of ML at the embedded edge by supporting any model, any neural network, any framework for any workload. Any resolution and any frame rate that comes through any sensor is efficiently compiled by the software and effectively deployed on the purpose-built ML device.

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