IEDM2024|Comprehensive Optimization of GPU AI Chips

Introduction

In recent years, the development of GPU-based artificial intelligence (AI) computing has progressed at a pace far exceeding the traditional Moore’s law scaling. While conventional chips face multiple scaling challenges, GPU AI computational performance has demonstrated remarkable growth—about 1000-fold over the past decade. This dramatic improvement stems from systematic optimizations across technology, chip design, system architecture, and algorithms.

Let us first examine figure 1 and figure 2, which highlight the contrasting scaling trends.

Figure 1: Slowing pace of traditional CPU scaling.

Figure 2: Comparative growth of CPU and GPU AI performance, highlighting how GPU technology has surpassed Moore’s law through comprehensive optimization strategies.

Evolution of GPU Architecture and Performance

The B200 is currently the world’s largest and most powerful GPU for high-performance computing and AI, representing the latest advancement in GPU technology. This product consists of two GPU chips placed side by side on a silicon interposer and interconnected via metal interconnects. Each GPU die is fabricated using TSMC's custom 4NP technology, with an area of 790.5 mm² and housing 208 billion transistors.

Figure 3: B200 GPU for high-performance computing/AI, with detailed comparison against its predecessor H200, showing significant improvements in multiple performance metrics.

The B200 architecture introduces several innovations, including second-generation transformer engines capable of handling precision formats as low as FP4. Its integration with 192 GB of HBM3E memory achieves 8 TB/s bandwidth, delivering 20 PFLOPS of sparse FP4 tensor performance under a 1000 W power envelope.

Figure 4: Linear scalability of GPU clusters.

Figure 5: Sparse matrix computation achieving 2× speed-up, demonstrating efficiency in modern GPU architecture.

Energy Efficiency and Computational Optimization

Energy efficiency is a core consideration in modern GPU design. The implementation of various optimization techniques has significantly enhanced both power consumption and computational efficiency.

Figure 6: Detailed breakdown of energy consumption across different compute functions.

Figure 7: Comprehensive comparison of compute cost—including power and die area—from 45 nm to 5 nm technology nodes.

The introduction of tensor cores has revolutionized computational efficiency, offering 1.5–4× power efficiency compared to conventional compute operations. This improvement is especially notable in mixed-precision computations.

Figure 8: Tensor cores delivering superior power efficiency over standard operations.

Figure 9: Performance and performance-per-watt gains using FP16 tensor cores with iterative optimization techniques.

Advances in Memory and System Integration

As AI accelerates, memory capacity has become increasingly important in AI computation, leading to a significant rise in GPU memory capacity. This growth is critical for supporting large language models and other complex AI applications.

Figure 10: Exponential growth trend in GPU memory capacity over time.

Figure 11: Hybrid gain cell technology achieving 3× the density of HD SRAM at comparable speed.

Emerging Technologies and Development Directions

The industry continues to explore innovative solutions to enhance GPU performance and efficiency. Advanced packaging and novel interconnect technologies are becoming key drivers of future performance scaling.

Figure 12: RC18 accelerator with 36-chip multi-chip module (MCM) design.

Figure 13: Co-packaged optics for long-range signal transmission.

Figure 14: Photonic engine architecture for next-generation interconnects.

Through this comprehensive optimization approach—from foundational technology development to system-level integration and algorithm improvements—GPU AI computing continues its rapid advancement. These innovations collectively achieve Huang’s law: doubling performance annually while maintaining power efficiency and reliability.

As new technologies in memory, interconnect, and packaging continue to evolve, GPU AI computing will further enhance its performance trajectory. The principle of cross-domain comprehensive optimization will remain a driving force for this remarkable growth.

Reference

[1] J. R. Hu et al., "Co-Optimization of GPU AI Chip From Technology, Design, System and Algorithms," in 2024 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2024.