Bipin Thomas, President
While manufacturers across the globe embark on the industry 4.0 journey, they do bear in mind that it’s not going to be smooth sailing. Picture this—an entire manufacturing supply chain on an autonomous mode wherein robots are managing production lines, autonomous vehicles transporting finished products to warehouse, and drones scanning shipments, and more. Sounds like an ideal autonomous manufacturing setting, right? However, to disrupt the manufacturing status quo by delivering such efficiencies on the shop floor, one needs to shift focus from software optimization to facilitating innovation on the hardware front. To put it succinctly, manufacturing enterprises need hardware accelerators and hardware-software converged solutions to enable better performing machine learning (ML) or artificial intelligence (AI) at the edge. This what Silicon valley-based ICURO excels at. “We are a cut above the other players in the market because we are bringing in the much-required hardware innovation that is necessary to deliver AI and ML at the edge, instead of solely laying emphasis on the software component,” begins Bipin Thomas, president at ICURO. “We are working with large hardware companies like Nvidia to equip manufacturers with higher performance from the hardware-software convergence perspective.”
Drawing on the wealth of experience it has garnered by delivering intelligent technology platforms to multiple industries using IoT and big data analytics, ICURO has cut corners in the autonomous manufacturing space. In addition, ICURO brings expertise in the regulatory requirements pertaining to data privacy and security owing to its experience in the digital health ecosystem.
On realizing the need for machine learning, advanced vision, and autonomous navigation in manufacturing, be it the shop floor or production lines, ICURO has brought a significant breakthrough in the industry with sensors and combined hardware-software accelerators similar to those used in autonomous cars. “We leveraged sensors used in self-driving cars, such as LiDAR, 4K camera, IMU, GPS, and more, and put them together to deliver new autonomous features in a factory floor setting, thereby improving the productivity of manufacturing companies while refining the quality of productions,” explains Thomas. Besides, ICURO built this entire stack in Nvidia chipset using enterprise-grade sensors and the latest GPU technology to offer these scalable and cost-effective autonomous systems to manufacturing organizations.
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.