Hello everyone, my name is Prashanth and I'm working as a staff RTL design engineer with 12 years of experience in VLSI design and development. Today I'm thrilled to present revolutionizing HPC architecture where we will explore how advancements in interconnects, system on chip integration, and energy efficient designs are transforming high performance computing systems. Let's begin by examining the HPC market's remarkable growth. High performance computing leverages multiple computers to perform complex calculations and data analysis that exceed the capabilities of a single machine. It is widely applied across science, engineering, businesses, and other fields. The global HPC market is projected to reach 55. 6 billion by 2026. growing annually at 7. 5 percent rate due to increasing demand from areas like AI, scientific research, and big data analytics. Modern HPC systems managing workloads spanning over 100, 000 processing cores have enabled breakthroughs in fields such as climate modeling, drug discovery, and AI. and astrophysics. In the era of artificial intelligence with innovations like chat gpt hpc systems are pivotal for training neural networks by processing massive data sets optimizing models and significantly reducing training times. These advancements support distributed computing innovations driving the development of larger and more sophisticated AI architectures. The rapid growth necessitates new architectural innovations to handle increasing, increasing workloads effectively. Scalable interconnects are essential in ensuring high speed data transfer, seamless parallel computing, reduced latency, and efficient scalability, empowering HPC systems to address complex applications with unmatched efficiency. Interconnects are the backbone of HPC systems, enabling high speed communication between components, and their evolution has significantly impacted real world applications and problem solving capabilities. Previous interconnects supported speed up of up to 100 Gbps, which while impressive, introduced latency at the millisecond scale. This was sufficient for many traditional HPC applications like basic weather simulations, genome sequencing, or engineering simulations. However, these speeds limited the real time processing and scaling required for emerging fields such as AI training and big data analytics. Early stage climate modeling could analyze broader weather patterns, but struggled to simulate localized events like tornadoes or urban heat islands due to data transfer and processing bottlenecks. Today's interconnects deliver speed of up to 400 Gbps with sub microsecond latency, representing a four times improvement in bandwidth and a drastic reduction in communication delays. This enables. Faster, more accurate simulations and support emerging technologies like AI, real time big data analysis and high fidelity simulations. The large scale AI models like GPT are trained faster and with greater efficiency. Real time updates and adjustments during training are feasible, drastically reducing model development times. Training a model like Chad GPT, which previously took weeks, can now be completed in days. This improves access to cutting edge AI solutions in healthcare, finance, and customer service. These interconnects enable population scale genomic analysis, facilitating personalized medicine by identifying genetic markers and potential treatment faster. Not just that, you would see advancements even for material science and engineering. The real time simulations of materials under stress. or in extreme conditions enable industries like aerospace and automotive to design safer, more efficient components. Engineers can now simulate the aerodynamics of an entire vehicle in hours, instead of days, speeding up the design and testing cycle. This leap in interconnect technology allows HPC systems to address problems previously considered computationally infeasible. Revolutionizing fields from AI to personalized medicine and global climate solutions. Such advances in interconnects are crucial for scaling HPC performance, but energy efficiency is equally important. Let's delve into low power SoC designs. Energy efficient SoC designs are redefining HPC capabilities by balancing performance and power consumption. I have highlighted these as some of the most important parameters in SoC design. Power reduction. Modern designs achieve up to 75 percent power savings compared to traditional architectures. For example, Cloud providers such as AWS use custom SOCs like Graviton processors, reducing energy costs while maintaining high availability. Performance density, delivering 2. 5 teraflops per millimeter square, a three times improvement over discrete solutions. NVIDIA's SOC based GPUs are used in AI workloads, such as autonomous driving, enabling real time processing in compact modules. Integration, CPUs, GPUs, and FPGAs are now integrated on a single chip, enhancing efficiency and functionality. For instance, AMD's EPYC SOSes combine processing and memory bandwidth, allowing data centers to consolidate tasks and reduce server footprint significantly. These innovations enable HPC systems to achieve high performance. computational throughput without excessive energy costs, paving the way for advancements in AI, scientific simulations and real time analytics. Now let's look at how packaging technologies support these achievements. To overcome scaling limitations, advanced packaging methods have revolutionized chip integration. 3D stacking This increase increases integration density by vertically stacking chip components, reducing interconnect distances, for example, HBM high bandwidth memory used in NVIDIA GPUs employ 3D stacking to deliver ultra fast memory access for AI and HPC workloads. Chiplet designs allow modular assembly of components, improving yield and enabling scalability. AMD's Ryzen processors use chiplet designs to combine multiple CPU cores with high efficiency, significantly enhancing multi core performance while keeping manufacturing costs manageable. Such packaging innovations are critical for building the next generation of HPC systems, enabling breakthroughs in fields like climate modeling, genome analysis, and large scale AI training. HPC advancements have transformed how we approach complex challenges across industries, supported by these advanced packaging technologies. 100 million transistors per millimeter square is achieved through 3D stacking and chiplet designs, enabling more powerful and compact systems. For example, AI training models like GPT, Leverage dense 3D stacked memory to process petabytes of data efficiently, reducing latency and enhancing throughput in data centers. 40 percent improvement over conventional 2D designs, overcoming traditional Scaling limitations and supporting scalability and energy efficiency. Weather forecasting systems now utilize HPC platforms with these innovations to run higher resolution models that predict climate changes faster and more accurately. These achievements provide the foundation for next generation HPC systems driving breakthroughs in areas such as personalized medicine, real time financial modeling, and autonomous vehicle simulation. Which we will explore next. Next generation HPC systems are designed to meet the demands of increasingly complex workloads. These systems provide high performance, delivering exceptional computational power for diverse and intensive workloads. For instance, Frontier, the world's first exascale HPC system, enables simulations of molecular dynamics for drug discovery, achieving performance levels surpassing one exaflops. Energy efficiency, maintaining power consumption within a sustainable envelope of under 30 megawatts, critical for large scale data centers. This efficiency is exemplified by systems like Japan's Fugaku, which uses energy efficient arm based processes to balance extreme computational power with low energy costs. Scalability, architected to grow with modern application demands. Ensuring seamless adaptation to future needs. Cloud providers like Microsoft, Azure, HPC Incorporate, flexible scalability, enabling research teams to add compute resources dynamically. Supporting projects like genome sequencing or AI model training without infrastructure bottlenecks. Let's look at some of the real world applications. Although we talked about so many high computing products in earlier slides, let's just take a look at a few more real world applications. Scientific computing facilitating complex simulations in physics, chemistry, and biology. For example, HPC systems are used to model black hole dynamics in astrophysics or simulate protein folding in drug discovery, drastically reducing the time needed to for breakthroughs. AI training. Accelerating the development and deployment of advanced machine learning models. For instance, OpenAI's models like GPT rely on HPC platforms to train on trillions of parameters, enabling state of the art natural language processing and computer vision applications. Data analytics. processing massive data sets for actionable business and research insights. Retail giants like Amazon use HPC driven analytics to optimize supply chain and personalize customer experience, while Genomic Research Institute analyzes terabytes of sequencing data to identify genetic markers for diseases. With a 60 percent performance per watt gain and a 3 times computational density increases, these systems enable faster drug discovery by modeling billions of compounds, accelerate AI breakthroughs like autonomous vehicle navigation, and process real time financial analytics or fraud detection, all while significantly reducing energy costs. Let's now look at some of the challenges these systems face and future directions for innovations. Thermal management, high density chip. Chips generate significant heat, necessitating advanced cooling solutions. For instance, liquid immersion cooling is being adopted in data centers, reducing power usage by up to 30 percent compared to traditional air cooling, while thermal aware chip designs prevent performance degradation under heavy workloads. Interconnect scaling. Achieving data transfer speeds beyond one terabits per second with minimal latency is essential for complex workloads like AI training and simulations. Optical interconnects, such as silicon photonics, offer high bandwidth and lower energy consumption, enabling seamless communication in next generation HPC architectures. Software optimization, advanced tools and frameworks must fully utilize heterogeneous resources like CPUs, GPUs, and FPGAs. For example, NVIDIA's CUDA and AMD's ROCM frameworks allow developers to optimize AI and scientific workloads, achieving up to 10 times performance improvements through efficient workload distribution and risk. resource management. Addressing these challenges require collaborative efforts across academia, industry, and governments, ensuring the continuous evolution of HPC systems to meet future demands. We kind of talked about the industry impact all across our talk and covered these points in one way or the other. You can put most of those, those technology breakthroughs in exascale computing and AI acceleration category. While all the big tech giants are working on these systems, they are committed to reduce the carbon footprint of the data centers and supercomputers. Computers like Google and Microsoft are investing in renewable energy power data centers and adopting carbon neutral strategies such as utilizing liquid cooling and AI for energy optimization, reducing emissions while maintaining performance. HPC systems are driving innovation, competitiveness, and productivity across sectors. For example, they enable small and medium enterprises to leverage advanced analytics for market insights, boost manufacturing efficiency through predictive maintenance, and accelerate R& D in industries like pharmaceuticals and aerospace. Such contributions make HPC systems indispensable for tackling global challenges and fostering technological progress. Now let's conclude by looking ahead to the future of HPC. HPC systems entering a new era of innovation. Marked by advancements in interconnects, SOC designs, and packaging technologies, the focus remains on sustainable computing, balancing performance improvements with energy efficiency by integrating AI driven energy management. For example, metadata centers use AI for dynamic workload optimization, achieving up to 38 percent energy savings. Collaborative research. Building strong partnerships between academia, industry, and government to drive breakthroughs. Initiatives like the European Processor Initiative aim to develop energy efficient, scalable processors for exascale HPC systems. Fostering innovation in fields like climate modeling and personalized medicine. These efforts ensure HPC systems remain at the forefront of science, technology, and industry, solving today's challenges. from pandemic response modeling to autonomous systems development while paving the way for a sustainable and technologically advanced future. Thank you.