AMD | Advancing AI with Energy-Efficient Architectures: Innovations in Fab Process, Packaging, and System Integration

Introduction

The rapid development of artificial intelligence (AI) has placed extremely high demands on computational power and energy efficiency. As AI models become increasingly complex, computing requirements have grown significantly, posing substantial power supply challenges for data centers and edge devices. This article explores innovations in semiconductor technology and explains how these advancements enable more energy-efficient AI architectures [1].

Advanced Memory Integration and Optimization

One of the fundamental challenges in AI computing is achieving efficient data transfer between memory and computing units. In traditional architectures, memory and computing components are separate, leading to significant power consumption due to data movement. Modern solutions employ innovative packaging technologies to position memory closer to compute units.

Figure 1 illustrates memory optimization strategies, showing the evolution from baseline designs to high-bandwidth memory (HBM) 3D stacking. The figure highlights the importance of reducing the distance between memory and compute units and utilizing low-power interconnects to enhance power efficiency.

The semiconductor industry has developed multiple memory integration optimization solutions. Starting from baseline configurations, manufacturers have expanded on-chip cache capacity through integration, eventually adopting high-bandwidth memory with advanced 3D stacking technology. This progression demonstrates a clear path toward more efficient memory architecture.

Figure 2 compares 2.5D and 3D memory stacking approaches, showing how 3D stacking achieves additional power savings and density improvements through vertical integration.

The transition from 2.5D to 3D stacking is a significant advancement in memory integration. While 2.5D technology uses a silicon interposer for horizontal connections, 3D stacking enables direct vertical integration of DRAM layers above processor cores. This vertical integration minimizes physical data transfer distances, improving power efficiency and memory bandwidth.

Advanced Packaging Technologies

Modern AI accelerators require sophisticated packaging solutions to efficiently integrate various components. AMD's MI300 series exemplifies breakthroughs in advanced packaging technology, realizing complex chiplet-based designs. This architecture consists of multiple accelerator complex die (XCD) based on AMD's CDNA 3 compute units, working alongside an input/output die (IOD) equipped with precision memory controllers and caching systems. The IOD integrates a 128-channel HBM3 interface and 256MB Infinity Cache, providing ultra-high memory bandwidth and efficiency.

Figure 3 illustrates the advanced architecture of the AMD MI300, showcasing the integration of XCD, IOD, and HBM3 memory using AMD's Infinity Fabric technology. This unifies GPU and CPU chiplet architectures, achieving high throughput.

Infinity Fabric interconnect technology is at the core of this architecture, enabling seamless communication between components while maintaining power efficiency. The integration of HBM3 memory using advanced 3D and 2.5D packaging techniques represents a significant improvement in memory subsystem design. The 2.5D silicon interposer technology provides high-bandwidth connections between memory and compute dies while maintaining optimal power characteristics.

Figure 4 presents a detailed block diagram of the MI300, illustrating the distribution of Infinity Cache across the IOD and an actual MI300X chip image, demonstrating the precise integration of various components.

The Infinity Cache implementation in MI300 features meticulous partitioning and distribution strategies. The cache is evenly distributed across the four IODs, with each IOD further subdivided into 64 1MB tiles. Each HBM channel is assigned two tiles, creating a localized data movement pattern that ensures efficient data access while maintaining power efficiency.

Process Technology Optimization

Process technology plays a critical role in achieving energy efficiency. Modern high-performance computing relies on innovative transistor designs and voltage regulation techniques to optimize both dynamic and static power consumption. The interaction between threshold voltage (Vth) and supply voltage (Vdd) presents complex optimization challenges that must be carefully managed.

Figure 5 illustrates power optimization achieved through field-effect transistor Vth adjustment, depicting the relationship between threshold voltage, supply voltage, and energy efficiency.

Engineers must carefully balance multiple parameters to achieve optimal power efficiency. The relationship between Vth and Vdd highlights the complex interplay affecting both dynamic and static power consumption. While lowering the supply voltage can reduce dynamic power, it must be balanced against the need to maintain sufficient noise margins and prevent timing violations. The Vth optimization process requires careful consideration of process variations, temperature effects, and reliability requirements.

Advanced 3D Integration

AMD's 3D V-Cache technology represents a major breakthrough in advanced three-dimensional integration. This innovative approach utilizes direct copper-to-copper bonding, eliminating traditional solder bumps and improving electrical and thermal characteristics. Through through-silicon via (TSV) technology, high-bandwidth vertical connections between dies are achieved while maintaining signal integrity and power efficiency.

Figure 6 illustrates AMD's 3D V-Cache technology, highlighting the bump-less copper-to-copper design and TSV-enabled vertical connectivity, showcasing its contributions to density and energy efficiency advancements.

This technology incorporates structural silicon layers, enhancing mechanical stability and thermal performance. The bump-less design reduces the overall stack height, improving thermal dissipation by minimizing thermal resistance between dies. This precise integration method enables significant increases in cache capacity and bandwidth while maintaining power efficiency.

Thermal Management and Power Delivery

Effective thermal management is essential for maintaining optimal performance in high-power AI accelerators. Modern designs employ deep trench capacitors, which provide better voltage droop mitigation than traditional planar capacitors. These structures are directly integrated into silicon, offering localized charge storage to maintain stable voltage levels during high-current transients.

Advanced power management techniques use distributed sensors to monitor thermal conditions and power consumption in real time. These data support sophisticated dynamic voltage and frequency scaling algorithms, which optimize performance while maintaining safe operating temperatures. The adoption of new thermal interface materials with improved thermal conductivity further enhances heat dissipation from the die to the heatsink.

Future Directions

The semiconductor industry is continuously advancing energy-efficient computing through multiple emerging technologies. Co-packaged optics represent a new approach to improving network bandwidth and energy efficiency in data center applications. This technology integrates optical transceivers directly into the processor package, reducing the energy required for high-speed data transmission.

Silicon photonics integration is advancing rapidly, with new technologies enabling the integration of optical interconnects into conventional CMOS processes. Advanced process nodes are exploring complementary FET (CFET) technology, which offers improved electrostatic characteristics and reduced parasitic capacitance compared to traditional transistor designs.

New memory technologies are being developed for caching applications, such as spin-transfer torque magnetic RAM (STT-MRAM), which provide potential energy savings while maintaining high performance. These innovations, combined with continued advancements in packaging and thermal management, will enable AI systems to meet increasing computing demands while maintaining energy efficiency.

References

[1] M. Fuselier, L. Bair, D. Kulkarni, G. Refai-Ahmed, J. Wuu, and O. Zia, "Advancing AI with Energy-Efficient Architectures: Innovations in Fab Process, Packaging, and System Integration," in 2024 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2024.