The Infosys High Tech practice helps semiconductor Original Equipment Manufacturers (OEMs), Original Design Manufacturers (ODMs), integrated device manufacturers, fabless designers, and independent software vendors adopt cloud computing. We migrate resource-intensive applications and engineering workflows across research, design, development, and manufacturing to private, public and hybrid cloud environments.
On-demand access to compute and storage resources empowers design and fabrication teams to manage large volumes of data and dynamic workloads while boosting productivity. It also enables multiple design teams to collaborate on exploration, compilation, synthesis, and place-and-route builds. Significantly, the cloud facilitates subscription-based consumption of CRM, ERP, product design, and artificial intelligence software.
Cloud-hosted frameworks allow enterprises to apply generative design algorithms, emerging production systems such as 3D printing, and big data engines for machine learning and deep learning. In addition, it supports ‘digital twinning’ to identify bottlenecks, predict failures, and improve process efficiency. Further, cloud solutions allow data scientists to prepare data for building, training and deploying cognitive automation models, which is an imperative for a digital factory.
Infosys cloud managed services span provisioning of Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), Enterprise-as-a-Service (EaaS), Database-as-a-Service (DBaaS), and Disaster Recovery-as-a-Service (DRaaS). Our data encryption methods, tools for secure transfer of chip layout design, industry-specific control mechanisms, and audit services address reliability as well as latency issues in cloud-based silicon design and development.
We develop custom APIs for cloud to cloud migration and integration of as-a-service models of diverse cloud providers, including AWS Cloud, Microsoft Azure, and Google Cloud Platform. We combine DevOps automation tools and open source technologies, such as Spark, Hadoop and Hive, to simplify backend services, improve service quality, and maximize cloud architectures. Notably, our dashboards for real-time monitoring of cloud services eliminate over-provisioning of compute resources.
