Terence S.-Y. Chen
Latitude Design Systems
Abstract
The rapid emergence of AI workloads like large language models is driving tremendous demands for more compute capability and efficiency in data centers. However, the pace of evolution in these AI models far outpaces the rate of advancement in hardware. This paper reviews the seismic impacts across the chip design landscape and architectural innovations needed to close the widening gap. It highlights key challenges around performance, power, thermals, data movement and flexibility. Advanced packaging, new interconnects, software-hardware co-design and early optimization will be critical to overcoming these hurdles. While difficult tradeoffs remain, creative solutions can help usher in the next era of AI acceleration.
Introduction
In recent years, artificial intelligence (AI) has exploded in popularity and usage. The introduction of large language models like ChatGPT has captivated the public imagination. But this insatiable demand for AI compute power has driven a need to optimize data center processors [1]. However, as Mutschler describes, the rapid evolution of algorithms makes this a moving target. Chip architects must build in flexibility for continuous change. The pace of advancement on the software side is outpacing silicon capability improvements dictated by Moore’s Law. This is forcing creative packaging techniques like 3D stacking to increase compute density. But it also creates new complexities across the design flow.
Optimizing for AI workloads involves difficult tradeoffs between power, performance, area (PPA) and time-to-market. Data movement and thermals are becoming major bottlenecks. This paper provides an overview of the paradigm shifts driven by AI and insights into navigating the resulting architectural challenges.
The Soaring Computational Needs of AI
The advent of large language models represents an unprecedented inflection point in data center compute demands. Whereas hardware typically improves incrementally, AI training workloads double every few months. This astonishing pace strains the limits of semiconductor advancement. AI algorithms require immense compute intensity and memory capacity. Power consumption is also critical, especially for training. And inference queries need to process vast volumes at low latency. AI chip designers must balance these extreme and ever-changing demands [2].
Ripple Effects Across the Design Flow Optimizing processors for AI imposes huge implications across the entire design process. Exploration must occur much earlier using real workloads to evaluate power/performance tradeoffs. Thermal and power analysis also needs to shift left. Multi-die integration is becoming more prevalent, necessitating advanced packaging expertise. On-chip data movement emerges as a central bottleneck. There is tension between customization for specific algorithms and flexibility for accommodating algorithm changes. Time-to-market pressures further complicate matters.
Navigating the Architectural Possibilities
Several architectural decisions are key in AI chip design. Choosing compute engines and memory types has cascading impacts. Partitioning to chiplets provides density but demands high-bandwidth, low-latency interconnects. Data movement requires optical I/O or silicon photonics. Thermal design necessitates intelligent sensors and throttling. Power budgets dictate advanced sleep schemes. Support for rapid reconfiguration and fine-grained testing provides algorithmic agility. As algorithms evolve, flexibility is paramount.
The Widening Technology Gap
A central challenge highlighted is the gap between the rapid pace of AI model advancement versus the slower hardware progress. While silicon scaling has slowed, off-chip communication remains expensive. AI evolution is too quick for multi-year processor design cycles. This demands new architectures, packaging and software integration to achieve the scale-out needed. But more chiplets create power and data bottlenecks. Flexibility, modularity and co-design are essential to keep pace.
Remaining Hurdles to Overcome Many hurdles remain in designing performant and efficient AI chips. Thermal and power challenges must be addressed, potentially requiring new materials or sophisticated sensors. On-chip data movement limitations need creative solutions like advanced interconnects or silicon photonics. The extensive software exploration required early in the design flow poses tooling and methodology challenges. Partitioning to optimize workloads between specialized hardware, chiplets and general purpose processors remains difficult. Rapid design iteration and automation will be critical moving forward.
The Path Forward
To navigate the paradigm shifts driven by artificial intelligence, chip architects will need to embrace new architectures, advanced packaging, and tighter software-hardware integration. Companies that can straddle both hardware and software domains will have a competitive edge. Modularity, flexibility and new methodologies will be key enablers. While difficult tradeoffs persist, the AI revolution also presents immense opportunities, propelling the industry into the next era of data center computing. With creative solutions, chip designers can help unleash AI’s untapped potential.
AI Challenges for Data Center Chips
AI is creating significant gaps between the pace of technology advancement and customer demands in the data center market. The emergence of large language models like ChatGPT and Dall-E have dramatically increased the need for more data processing, lower latency, improved power efficiency, and greater functionality in AI chips [3]. However, the rapid evolution of AI models also requires built-in flexibility to accommodate continuous algorithm changes.
AI processors for data centers need to provide high throughput for inference queries while remaining within stringent power budgets. Training workloads are even more computationally intensive and can consume megawatts of power. On-chip data movement and communication emerges as a key bottleneck that must be optimized. Thermal issues are exacerbated by high-density advanced packaging techniques. AI chip designers have to balance myriad conflicting constraints around performance, power, costs, time-to-market, and algorithmic flexibility.
AI Solutions for Data Center Chips
Many novel architectures are emerging to meet the demands of AI in the data center. For inference applications, server CPUs are being augmented with GPUs, TPUs (Tensor Processing Units), and FPGAs for acceleration. New memory technologies like High Bandwidth Memory (HBM) and Compute Express Link (CXL) provide higher bandwidth and new topologies. Advanced 2.5D and 3D packaging integrates more compute dies using silicon interposers and through-silicon vias (TSVs). This allows chiplet partitioning, where different functions are implemented on separate dies. Optical I/O reduces data movement bottlenecks, enabled by progress in silicon photonics [4]. Startups are exploring radically new AI accelerator architectures tailored to specific models. The proliferation of AI workloads is driving a new wave of specialized heterogeneous computing.
AI Tradeoffs for Data Center Chips
Optimizing the performance, power, and area/cost of AI chips involves navigating difficult tradeoffs. More on-chip memory capacity improves data locality and reduces external bandwidth needs, but adds cost. Higher memory bandwidth boosts performance but increases power draw and thermal issues. Low latency interconnects like NVLink require extra pins and die area. New advanced packaging brings density improvements but also reliability risks and yield loss.
More specialized AI accelerators reduce top-level data movement but limit flexibility. There are always tensions between custom hardware for specific algorithms vs. programmable platforms that support more algorithmic agility. AI chip architects must strike the right balance across these competing constraints.
The Role of Silicon Photonics
The article specifically highlights silicon photonics as a potential solution to improve the communication speed and efficiency between different AI chips in data centers. Silicon photonics uses light to transmit data instead of electrical signals, which can reduce power consumption and latency. This helps address the growing communication and data movement bottleneck as more partitioning and chiplets are used in AI designs, resulting in more integration and coordination needs between components. The article suggests silicon photonics could alleviate this key bottleneck impacting system performance [5].
Remaining Challenges
Based on the article, some of the key remaining challenges moving forward for AI chip design include:
Closing the widening gap between the rapid pace of AI algorithm and model advancement versus the slower improvement in hardware capabilities.
Managing the thermals and power demands of high-density advanced packaging techniques.
Finding the right balance in the tradeoffs between performance, power, cost, time-to-market, and flexibility for future algorithm changes.
Overcoming the bottlenecks in on-chip and off-chip data movement and communication.
Developing new interconnects, chiplets, and technologies like silicon photonics to improve speed and efficiency.
Enabling much more extensive software exploration and power analysis with real workloads early in the design flow.
Achieving sufficient modularity and flexibility to accommodate the continuous evolution in AI models.
Partitioning workloads optimally between specialized hardware engines and general purpose processors.
Integrating AI-focused methodologies, tools and flows to automate and speed up the design process.
Fostering tighter collaboration between system architects, hardware designers, and software developers.
Exploring radically new AI-optimized architectures specialized for particular models or applications.
Conclusions
In conclusion, the emergence of AI represents a seismic shift for chip design. Rising to the challenge will require changes across architectures, advanced packaging, design methodologies, software integration, and tools. Companies that can master AI's demanding compute needs will be well-positioned competitively. But it will require bringing hardware and software worlds closer together. There are difficult tradeoffs ahead, but immense opportunities as AI propels computing into a new era. With creative solutions and a collaborative mindset, the industry can help unleash AI's full potential.
References
[1] B. Li, J. Gu and W. Jiang, "Artificial Intelligence (AI) Chip Technology Review," 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2019, pp. 114-117, doi: 10.1109/MLBDBI48998.2019.00028.
[2] B. Khailany et al., "Accelerating Chip Design With Machine Learning," in IEEE Micro, vol. 40, no. 6, pp. 23-32, 1 Nov.-Dec. 2020, doi: 10.1109/MM.2020.3026231.
[3] D. Amuru, H. V. Vudumula, P. K. Cherupally, Z. Abbas, et al., "AI/ML Algorithms and Applications in VLSI Design and Technology," February 2022. [Online]. Available: https://arxiv.org/ftp/arxiv/papers/2202/2202.10015.pdf. Accessed: October/23/2023. Licensed under CC BY 4.0.
[4] N. Margalit, C. Xiang, S. M. Bowers, A. Bjorlin, R. Blum, and J. E. Bowers, "Perspective on the future of silicon photonics and electronics," Appl. Phys. Lett., vol. 118, no. 220501, June 01, 2021.
[5] R. Gupta, R. Singh, A. Gehlot, S. V. Akram, N. Yadav, R. Brajpuriya, A. Yadav, Y. Wu, H. Zheng, A. Biswas, E. Suhir, V. S. Yadav, T. Kumar, and A. S. Verma, "Silicon photonics interfaced with microelectronics for integrated photonic quantum technologies: a new era in advanced quantum computers and quantum communications?," Nanoscale, vol. , no. 10, 2023.
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