IEDM2024|Memory Needs and Solutions for AI and High-Performance Computing

Introduction

With the rapid development of artificial intelligence (AI) and high-performance computing (HPC) technologies, the demands on memory systems have been continuously rising. As AI models grow exponentially in scale and complexity, the need for computing power and memory resources has reached unprecedented levels. This paper explores the key memory requirements in AI and HPC domains and the emerging solutions addressing these challenges.

Figure 1: Exponential growth in computing requirements across various AI domains from 2001 to 2023, illustrating the historical evolution of compute demands for AI training.

Memory Performance Requirements

In modern computing systems, performance is closely tied to the interaction between compute capability and memory bandwidth. The roofline model is an effective tool to analyze this relationship, helping to understand performance limitations and optimization opportunities.

Figure 2: Roofline model showing the relationship between operational intensity and achievable performance, highlighting compute-bound and memory-bound regions.

The performance of a computing system is determined by its compute power and memory bandwidth. The ratio between these two factors defines the machine balance (Mb), while different applications exhibit different operational intensity (OI). The OI is calculated as:

OI = Number of Floating Point Operations/Number of Bytes

For memory-bound applications (OI < Mb), performance is constrained by memory bandwidth. For compute-bound applications (OI > Mb), performance is limited by computational capacity.

Memory Architecture Evolution

Memory solutions have evolved significantly along two primary trajectories: DDR evolution for CPUs and HBM evolution for GPUs.

Figure 3: Evolution of DDR memory technology, showing bandwidth and bus speed improvements form DDR4 to DDR5.

DDR technology has evolved from DDR4 to DDR5, with a significant increase in bandwidth:

• DDR4: 3200 Mbps → 25.6 GB/s bandwidth

• DDR5: 8400 Mbps → 67.2 GB/s bandwidth

HBM Technology has also seen significant improvements:

Figure 4: HBM evolution timeline, highlighting the bandwidth improvements from HBM2e to HBM3e.

HBM technology has evolved from HBM2e to HBM3:

• HBM2e: 460 GB/s

• HBM3: 819 GB/s

• HBM3e: 1.2 TB/s

Reliability Considerations

As systems scale thousands of components, reliability becomes increasingly critical. In distributed systems, a single uncorrectable memory error could affect thousands of GPUs.

Figure 5: Impact of on-die ECC on memory reliability, illustrating significant improvements in error tolerance with modern ECC implementations.

Modern memory systems implement several key reliability features:

• On-die ECC: Error correction implemented within DRAM devices

• Chipkill: Advanced ECC that can handle full-chip failures

• Error Prediction: Proactively identifies potential failures

• Scrubbing: Continuous memory scanning to detect and fix errors

Future Directions and Advanced Solutions

Memory systems for AI and HPC are evolving toward more complex architectures, integrating multiples technologies and methods.

Figure 6: Evolution of server architecture showing the transition from traditional to future memory configurations, incorporating disaggregated memory solutions.

Key developments include:

• Memory disaggregation via CXL (Compute Express Link)

• Advanced packaging solutions to improve density and performance

• Integration of heterogeneous memory technologies within a single system

• Near-memory computing capabilities

Figure 7: Advanced packaging technologies supporting next-generation memory solutions, showing various stacking and bounding techniques.

The industry is actively developing new packaging technologies to enable:

• Higher I/O density through advanced stacking

• Improved thermal management

• Better system-level integration

• Enhanced performance through novel interconnect solutions

Conclusion

The evolution of memory systems continues to be driven by the demands of AI and HPC applications. Addressing these challenges requires a balanced approach that incorporates:

• Heterogeneous memory solutions combining HBM, DDR, and emerging technologies

• Implementation of robust reliability features for system stability

• Development of advanced packaging and interconnects

• Adoption of memory disaggregation and near-memory computing strategies

As AI models keep expanding and HPC applications become more demanding, the industry must continue to innovate, delivering memory capacity while maintaining system balance and reliability.

Reference

[1] E. Confalonieri, "Memory Needs and Solutions for AI and HPC," in IEDM 2024 Short Course on AI Systems and the Next Leap Forward, Short Course 2.3, 2024.