Chiplets And New Tools Transform Chipmaking

Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I am Varadarajan Parthasarathy, PhD in Physics with applications in astronomical sciences. In the present, I am focused on domains such as semiconductors, high-performance computing, and artificial intelligence.

Q2. What are the key challenges in scaling domain-specific GPUs, ASICs, and accelerators for generative AI training and high-performance computing workloads?

One of the main challenges I see is managing power and thermal requirements. As power density increases at advanced nodes, cooling becomes a significant issue, specifically in dense server environments. Ensuring effective power delivery and heat dissipation is often a primary bottleneck.

Memory bandwidth and hierarchy are also critical. Large models and datasets put significant pressure on memory subsystems. In my experience, developing optimized high-bandwidth memory interfaces and new memory hierarchies is both complex and costly.

Scalability is another area that demands focus. Moving beyond a single chip means we need ultra-high-bandwidth, low-latency interconnects. Attaining effective parallelism across many accelerators is a significant systems challenge.

Software and programmability are just as important as hardware. Building robust software stacks that can fully utilize the hardware and are easy for developers…