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High performance computing applications are being developed constantly. The ability to utilize computing power to process data at high speeds has engendered a world of new possibilities for chip development and other technology, research, and development initiatives. 

Here, we will discuss how organizations can address chip design challenges and leverage cloud computing advantages with high performance computing.

Challenges of Chip Design Solved Through HPC

Semiconductor development becomes more challenging by the year. As competition increases, transistors become smaller and each new process technology packs more onto each die. Consequently, chip design companies must innovate to remain competitive.

In chip development, advanced technology node issues, heat dissipation, floorplanning, place and route, logic synthesis, and function verification have pushed EDA tools to their limits. In modern age SOCs, organizations must account for hundreds of clock and power domain analysis to allow for IP blocks from a variety of sources.

Designing innovative chips requires handling these challenges while also operating under time-to-market constraints. Scaling aggressively to maintain performance and cost leadership, creating new architectural innovations, and working with increasingly faster interfaces are all essential practices for keeping pace with market requirements. 

Cloud-based applications can provide high bandwidth, density, and multiport memories to accommodate high performance computing and enable simulation, machine learning, and big data analysis capabilities that are embedded in the EDA applications.

High Performance Computing Applications Used in Chip Design

High performance computing enables effective scaling for teams focused on performance, capacity, and power efficiency in their chip designs. To accomplish these designs, new nodes and innovative design techniques are employed. EDA tools utilize HPC to handle increasingly complex designs and provide enhanced capacity and faster runtimes.

With high performance computing, designers can employ simulation and advanced verification methods to cross the technology-to-design gap and design-to-silicon gap, ensuring that once the device is on the market it performs as predicted in development.

Memory development turnaround time (TAT) denotes another essential aspect that must be considered. Adopting design technology co-optimization (DTCO) lets designers co-optimize new technologies and architectures before finalizing and ¡°hardening¡± choices. HPC can provide early awareness of potential reliability issues during the design phase for accelerating verification.

Saving Costs With Cloud Computing

Examining cost savings also reveals significant advantages for chip design. Costs for taping out chips include personnel and engineers, licenses, and compute costs. Rather than comparing the price of running an on-premises server with the same period of ¡°on-demand¡± cloud pricing, it is crucial to compare it to the larger picture. By utilizing the cloud, businesses get their chips out the door sooner. In addition, they can save on rent, electricity, physical security, hardware maintenance, and unused compute power. Engineers can also experiment with a variety of compute types in minutes without ordering specific hardware and waiting for installation. This all accelerates innovation and positions companies to achieve first-to-market goals.

With high performance computing applications that can be adopted incrementally, organizations do not need to start from scratch. Instead, companies can adopt a hybrid model and use their local resources while expanding into the cloud as needed.