In this webinar we will discuss the challenges in designing and manufacturing an electro-mechanical interface for testing a Integrated Circuit running at 224 Gbps data rates. We will discuss the roadblocks in working in a fully simulated environment and our products that are capable of meeting next generation Integrated Circuit requirements. Overall, deploying an optimized hardware infrastructure that combines high-performance computing systems, specialized AI accelerators, efficient interconnects, storage solutions, and software frameworks is essential to realize the full potential of next-generation AI chips.
We are on the edge of the greatest technological shift since the emergence of the internet. With the value and influence of generative artificial intelligence (AI) hard to measure accurately, with the potential to fundamentally change industries from automotive to scientific research and even medical treatment, are difficult to measure. Designing and manufacturing next generation data center hardware is required to ensure we realize the full potential of advanced AI semiconductor devices.
To meet the demands of ML and AI workloads, a transition to 224 Gbps PAM-4 transmission lines is paramount as foundational building blocks to unlocking the evolutionary leap to next generation data centers. To realize the full potential of these next-generation AI chips, specific hardware deployments are needed, and important considerations will be discussed during this webinar:
High-performance computing systems: These systems should have sufficient memory and storage capacity to handle large AI datasets efficiently and are most critical in delivered performance.
Specialized AI accelerators: AI chips need to be integrated into dedicated hardware infrastructure. This may involve deploying AI accelerators like TPUs, GPUs, or other AI-specific chips. High-speed interconnects: Efficient communication between CPUs, GPUs, and AI accelerators is vital to ensure seamless data transfer and processing. Storage solutions: AI workloads generate and process large volumes of data, so having fast and scalable storage solutions is crucial.
Software frameworks and libraries: To leverage the capabilities of next-generation AI chips, software frameworks and libraries like TensorFlow, PyTorch, or CUDA need to be integrated into the hardware deployments.
- Understand current technological trends on generative AI
- Explore the transition to 224 Gbps PAM-4 transmission lines
- Discuss specific hardware deployments needed to realize the full potential of these next-generation AI chips
- Discover Smiths Interconnect’s product offering