Custom Search
 


The SiMa machine learning System on a Chipo due for launch later this year
 
 
US based semiconductor designer SiMa.ai sets up R&D in Bangalore

Bangalore, January 29 2021: US-based, Indian talent-fueled  machine learning company  SiMa.ai ,  has set up its first offshore design centre in Bangalore – a prelude to launching later this year,  the world’s first purpose-built machine language-based System on a Chip (MLSoc), with significant applications in  vision computing, robotics, autonomous systems ans  automotive electronics.
The  product tape-out is expected in the second half of 2021, to be followed  by commercial production.The India-based   development will be led by Sudershan Vuruputoor, Senior Director,hardware development and Amit Kumar Mitra,  Senior Director, software  development, announced  SiMa.ai founder-CEO Krishna Rangasayee, at a virtual media event yesterday.
The new site will bolster the company’s engineering and operations team, opening up new in-region job opportunities for board development, operations, infrastructure, and system application roles,  Rangasayee said, adding: “The embedded edge market is ripe for disruption and machine learning has moved from nice to have to must-have. Our vision is to build our customers an innovative ML platform that ultimately helps scale the widespread adoption across important embedded edge applications, such as robotics, medical, and autonomous vehicles. The expansion into India is pivotal as we prepare to deliver the industry’s first purpose-built machine learning SoC.”
SiMa.ai's MLSoC platform environment enables the 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 machine learning device. The embedded edge market is a massive $1T+ market that’s been surviving on old technology for decades.
Read Rangasayee’s blog 2021: The Year of ML Scaling at the Embedded Edge here