The Nvidia hardware, in other terms AI on the edge, composes an integral part of ICURO’s autonomous edge stack as it contains the GPU required for processing and inferencing ML algorithms at the edge. Thomas adds, “We train neural networks on the cloud using more data and once the deep learning is done, we apply proven techniques of optimizing and pruning these neural networks for edge performance.” The third component is a range of enterprise-grade sensors that can be integrated with the edge stack to deliver real time decision-making capabilities. The sensors along with edge stack can control an autonomous vehicle to move manufacturing parts from storeroom to production lines. On a production line, a 3D vision enabled edge stack can provide 100 percent coverage on product quality, inspecting thousands of finished parts per minute with accuracy and repeatability. ICURO’s team of AI engineers assist clients in choosing high-value use cases with performance metrics that help to define new quality and productivity gains and then configure the autonomous edge stack to deliver the same. Lastly, Thomas underscores the importance of upscaling and training the emerging new collar workforce for industry 4.0. ICURO educates people on the skills necessary to work in tandem with robots and autonomous machines that provide hordes of intelligent decision data at their fingertips.

Currently working in the industrial, manufacturing, and semiconductor sectors, ICURO is all geared up for rolling out “Industry XAI” (XAI stands for eXplainable AI). “This comprehensive and transparent AI-centric business technology framework is going to be the future of autonomous manufacturing enterprises to launch new business models and outcomes. Our autonomous technology lab located in Santa Clara offers a unique hands-on infrastructure for hardware and software engineers to become autonomous system specialists,” ends Thomas.