Chapter 12: Future Trends and Emerging Technologies in GPU Design

As GPU architectures continue to evolve to meet the increasing demands of parallel computing workloads, several emerging trends and technologies are poised to shape the future of GPU design. In this chapter, we explore some of these key trends, including heterogeneous computing and accelerators, 3D stacking and chiplet-based designs, domain-specific architectures for AI and machine learning, and open research problems and opportunities in GPU architecture.

Heterogeneous Computing and Accelerators

Heterogeneous computing, which combines different types of processors or accelerators to achieve higher performance and energy efficiency, has become increasingly prevalent in recent years. GPUs have been at the forefront of this trend, often being paired with CPUs to accelerate parallel workloads. However, the landscape of accelerators is rapidly expanding, with new types of specialized hardware being developed for specific application domains.

One notable example is the rise of AI accelerators, such as Google's Tensor Processing Units (TPUs) [Jouppi et al., 2017], which are designed specifically for accelerating machine learning workloads. These accelerators often employ reduced-precision arithmetic, specialized memory hierarchies, and dataflow architectures to achieve high performance and energy efficiency for AI tasks.

Another emerging class of accelerators is focused on graph processing and analytics. Graph processing workloads, such as those found in social network analysis, recommendation systems, and scientific simulations, exhibit irregular memory access patterns and fine-grained synchronization, which can be challenging for traditional CPU and GPU architectures. Specialized graph processing accelerators, such as the Graphicionado [Ham et al., 2016] and the GraphCore Intelligence Processing Unit (IPU) [GraphCore, 2020], aim to address these challenges by providing hardware support for efficient graph traversal, synchronization, and load balancing.

As the diversity of accelerators grows, the challenge of integrating them into a cohesive system becomes more complex. Heterogeneous system architectures, such as AMD's Heterogeneous System Architecture (HSA) [AMD, 2015] and NVIDIA's CUDA Unified Memory [NVIDIA, 2020], aim to provide a unified programming model and memory space across different types of processors and accelerators. These architectures enable seamless collaboration between CPUs, GPUs, and other accelerators, allowing developers to focus on algorithm design rather than the intricacies of data movement and synchronization between different devices.

Research in this area explores topics such as efficient task partitioning and scheduling across heterogeneous devices, unified memory management, and high-performance interconnects for heterogeneous systems. As the landscape of accelerators continues to evolve, the design of GPUs will likely be influenced by the need to integrate seamlessly with other types of specialized hardware.

3D Stacking and Chiplet-Based Designs

3D stacking and chiplet-based designs are emerging packaging technologies that offer new opportunities for GPU architecture innovation. These technologies enable the integration of multiple dies or layers within a single package, allowing for higher bandwidth, lower latency, and more efficient power delivery compared to traditional 2D packaging.

3D stacking, such as through-silicon vias (TSVs) or hybrid memory cube (HMC) technology [Jeddeloh and Keeth, 2012], enables the vertical integration of multiple layers of logic or memory. This technology has been used in high-bandwidth memory (HBM) [Lee et al., 2014], which provides significantly higher memory bandwidth and lower power consumption compared to traditional GDDR memory. GPUs, such as AMD's Radeon R9 Fury X and NVIDIA's Tesla P100, have already adopted HBM to alleviate memory bandwidth bottlenecks in memory-intensive workloads.

Chiplet-based designs, on the other hand, involve the integration of multiple smaller dies (chiplets) within a single package using high-density interconnects, such as silicon interposers or embedded multi-die interconnect bridges (EMIBs) [Demir et al., 2018]. This approach allows for the mixing and matching of different process technologies, enabling the optimization of each chiplet for its specific function. For example, compute-intensive chiplets can be manufactured using advanced process nodes, while memory-intensive chiplets can use older, more cost-effective process nodes.

The modular nature of chiplet-based designs also enables more flexible and scalable GPU architectures. For instance, the number of compute chiplets can be varied to create GPUs with different performance and power characteristics, without the need for a complete redesign of the GPU. This approach can also facilitate the integration of specialized accelerators or memory technologies alongside the GPU compute chiplets.

Research in this area explores topics such as 3D-stacked GPU architectures, chiplet-based GPU designs, and novel interconnect technologies for multi-die integration. As process technology scaling becomes more challenging and expensive, 3D stacking and chiplet-based designs offer a promising path forward for continued performance and energy efficiency improvements in GPU architectures.

Domain-Specific Architectures for AI/ML

The rapid growth of artificial intelligence (AI) and machine learning (ML) applications has driven the development of domain-specific architectures optimized for these workloads. While GPUs have been the primary platform for AI/ML acceleration in recent years, there is a growing trend towards more specialized hardware that can provide higher performance and energy efficiency for specific AI/ML tasks.

One example of such specialized hardware is the neural processing unit (NPU), which is designed specifically for accelerating deep neural network (DNN) inference and training. NPUs often employ reduced-precision arithmetic, specialized memory hierarchies, and dataflow architectures that are tailored to the unique characteristics of DNN workloads. Examples of NPUs include Google's Tensor Processing Units (TPUs) [Jouppi et al., 2017], Intel's Nervana Neural Network Processors (NNPs) [Rao, 2019], and Huawei's Ascend AI processors [Huawei, 2020].

Another emerging trend in domain-specific architectures for AI/ML is the use of in-memory computing and analog computing techniques. In-memory computing architectures aim to reduce the energy and latency associated with data movement by performing computations directly in memory. Analog computing techniques, such as those used in memristor-based accelerators [Shafiee et al., 2016], leverage the physical properties of devices to perform computations in a more energy-efficient manner compared to digital circuits.

As AI/ML workloads continue to evolve and become more diverse, there is a growing need for flexible and programmable domain-specific architectures that can adapt to changing requirements. One approach to achieving this flexibility is through the use of coarse-grained reconfigurable architectures (CGRAs) [Prabhakar et al., 2017], which provide an array of programmable processing elements that can be reconfigured to support different dataflow patterns and algorithms.

Research in this area explores topics such as novel AI/ML accelerator architectures, in-memory and analog computing techniques, and programmable and reconfigurable architectures for AI/ML. As GPUs continue to play a significant role in AI/ML acceleration, the design of future GPU architectures will likely be influenced by the need to integrate more specialized hardware and adapt to the unique requirements of these workloads.

Open Research Problems and Opportunities

Despite the significant advances in GPU architecture and parallel computing in recent years, there remain many open research problems and opportunities for further innovation. Some of these challenges and opportunities include:

1. Energy efficiency: As the performance and complexity of GPUs continue to grow, improving energy efficiency becomes increasingly critical. Research opportunities in this area include novel circuit and architecture techniques for reducing power consumption, such as near-threshold computing, power gating, and dynamic voltage and frequency scaling.

2. Scalability: Enabling GPUs to scale to even larger numbers of cores and threads while maintaining high performance and programmability is a significant challenge. Research in this area may explore topics such as hierarchical and distributed GPU architectures, scalable memory systems, and programming models that can effectively harness the parallelism of future GPUs.

3. Reliability and resilience: As GPUs are increasingly used in mission-critical and safety-critical applications, ensuring their reliability and resilience becomes paramount. Research opportunities in this area include novel fault tolerance and error correction techniques, such as algorithm-based fault tolerance, checkpoint and recovery mechanisms, and resilient architecture designs.

4. Virtualization and multi-tenancy: Enabling efficient sharing of GPU resources among multiple applications and users is essential for cloud computing and data center environments. Research in this area may explore topics such as GPU virtualization techniques, quality-of-service (QoS) management, and resource allocation and scheduling algorithms for multi-tenant GPU systems.

5. Programming models and tools: Developing programming models and tools that can effectively harness the performance of future GPU architectures while maintaining programmer productivity is an ongoing challenge. Research opportunities in this area include domain-specific languages and compilers for GPUs, auto-tuning and optimization frameworks, and debugging and profiling tools for parallel programs.

As GPU architectures continue to evolve and new application domains emerge, researchers and engineers will need to address these and other challenges to unlock the full potential of parallel computing. By exploring novel architecture designs, programming models, and software tools, the research community can help shape the future of GPU computing and enable new breakthroughs in fields such as scientific computing, artificial intelligence, and data analytics.

Further Reading

For those interested in delving deeper into the topics covered in this chapter, we recommend the following resources:

1. Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., ... & Yoon, D. (2017). In-datacenter performance analysis of a tensor processing unit. In Proceedings of the 44th Annual International Symposium on Computer Architecture (pp. 1-12). https://dl.acm.org/doi/abs/10.1145/3079856.3080246 (opens in a new tab)

2. Ham, T. J., Wu, L., Sundaram, N., Satish, N., & Martonosi, M. (2016). Graphicionado: A high-performance and energy-efficient accelerator for graph analytics. In 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) (pp. 1-13). IEEE. https://ieeexplore.ieee.org/abstract/document/7783759 (opens in a new tab)

3. AMD. (2015). AMD Heterogeneous System Architecture (HSA). https://www.amd.com/en/technologies/hsa (opens in a new tab)

4. NVIDIA. (2020). CUDA Unified Memory. https://developer.nvidia.com/blog/unified-memory-cuda-beginners/ (opens in a new tab)

5. Jeddeloh, J., & Keeth, B. (2012). Hybrid memory cube new DRAM architecture increases density and performance. In 2012 symposium on VLSI technology (VLSIT) (pp. 87-88). IEEE. https://ieeexplore.ieee.org/abstract/document/6243767 (opens in a new tab)

6. Lee, J. H., Lim, D., Jeong, H., Kim, H., Song, T., Lee, J., ... & Kim, G. (2014). A 1.2 V 8 Gb 8-channel 128 GB/s high-bandwidth memory (HBM) stacked DRAM with effective microbump I/O test methods using 29 nm process and TSV. In 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) (pp. 432-433). IEEE. https://ieeexplore.ieee.org/abstract/document/6757501 (opens in a new tab)

7. Demir, Y., Pan, Y., Song, S., Hardavellas, N., Kim, J., & Memik, G. (2018). Galaxy: A high-performance energy-efficient multi-chip architecture using photonic interconnects. In Proceedings of the 28th ACM International Conference on Supercomputing (pp. 303-312). https://dl.acm.org/doi/abs/10.1145/2597652.2597664 (opens in a new tab)

8. Rao, T. (2019). Intel Nervana Neural Network Processors (NNP) Redefine AI Silicon. [https://www.intel.com/content/www/us/en/artificial-intelligence (opens in a new tab)