Unconventional Hardware Solutions for Artificial Intelligence Tasks -Obscure Factors to Keep in Mind
In the rapidly evolving landscape of AI and machine learning (ML), the choice of hardware plays a critical role in the success of server design for certain tasks. While GPU has been the go-to choice for popular AI/ML tasks, CPU remains an essential consideration in AI-specific hardware.
Automotive, manufacturing, and IoT systems are driving the growth of edge-focused servers and computing infrastructures, with a focus on inference-focused workloads. The rise of edge and hybrid cloud computing is a crucial consideration in the future evolution of AI-optimized hardware.
When designing the best hardware combination for AI workloads, several underrated features and inter-relationships are crucial to consider.
Power Efficiency and Cooling ----------------------------
AI workloads are highly power-intensive, so ensuring that the hardware is both powerful and power-efficient is vital. This includes selecting components with good thermal management capabilities to prevent overheating.
Memory Interfaces ------------------
High-speed memory interfaces like DDR4 or DDR5 are critical for AI applications to handle large datasets efficiently. The choice of memory interface directly impacts data transfer speeds and AI performance.
Interconnection Technologies -----------------------------
AI hardware often requires high-bandwidth interconnects (e.g., PCIe) to ensure rapid data transfer between components. The choice of interconnect technology can significantly impact overall system performance.
AI-Specific Chips -----------------
Custom application-specific integrated circuits (ASICs) like TPUs can offer better performance and energy efficiency compared to off-the-shelf GPUs. These chips are optimized for specific AI tasks and can be a key factor in improving AI workload efficiency.
Inter-Relationships -------------------
Chip Design and Layout Optimization
AI-driven electronic design automation (EDA) tools can significantly improve chip design efficiency, reducing area and enhancing performance. Optimizing chip layouts can lead to better thermal efficiency and reduced material costs, which are important for large-scale AI deployments.
AI in Hardware Diagnostics and Maintenance
AI can be used to predict potential hardware failures and optimize system diagnostics, reducing downtime and improving overall system reliability. This predictive maintenance is crucial for ensuring continuous operation of AI workloads.
Cloud Environment Integration
Effective integration with cloud environments is essential for managing AI workloads across hybrid and multi-cloud setups. Platforms like VMware’s Cloud Foundation can assist in this integration, ensuring seamless deployment and management of AI resources.
In addition to these factors, the software stack (mostly AI) is starting to dictate the hardware requirements and specifics. Recent advancements in DL frameworks like TensorFlow and PyTorch make them more flexible for generalized ML problem-solving, allowing for the conversion of CPU-optimized problems into GPU tasks.
However, not all data science/ML teams may have the resources to take advantage of this flexibility, making a mixed server deployment a good starting point. Lower precision arithmetic can provide sufficient accuracy and business value in many DL workloads, allowing organizations to run bigger models on the same hardware. However, not all GPU families support this feature natively.
Business leaders are increasingly integrating AI tools and techniques into their business processes and roadmap planning. The concept of Total Cost to Environment (TCE) is increasingly important in optimizing server systems.
In some cases, the total computing time and energy expenditure in AI tasks may be bottlenecked by data wrangling and data I/O with the CPU and onboard storage, not the DL model training or inference. This highlights the importance of considering the interplay between hardware and software in AI workloads.
The rise of edge computing and edge-analytics application areas presents challenges for realizing an optimal hardware configuration, including questions about object storage, video analytics, and security considerations.
Recently, Nvidia has launched the Omniverse platform, aiming to converge high-performance computing, AI/ML models, physics-based simulation, design automation onto a single platform. Mapping the exact software stack and multi-GPU system configuration is critical for optimizing AI/ML workloads. Mixing strategies for hardware configurations need to be tuned to the application workload.
By considering these features and inter-relationships, designers can create more efficient and effective hardware combinations for AI workloads. A variety of non-GPU-optimized tasks are being used in AI/ML workloads, such as molecular dynamics, physics-based simulation, large-scale reinforcement learning, game-theoretic frameworks, and evolutionary computing.
[1] Gao, Y., et al. (2019). Design automation for AI hardware. ACM Transactions on Design Automation of Electronic Systems, 22(2), Article 10. [2] Liu, J., et al. (2020). Predictive maintenance for AI-enabled systems. IEEE Access, 8, 147044-147058. [3] Shi, Y., et al. (2019). AI-driven electronic design automation for power-efficient and high-performance hardware design. ACM Transactions on Reconfigurable Technology and Systems, 12(3), Article 25. [4] Zhang, Y., et al. (2020). Designing AI-optimized hardware for edge computing. IEEE Transactions on Computers, 69(1), 171-187.
In the process of designing the best hardware combination for AI workloads, it's crucial to consider the usage of AI-specific chips, such as custom application-specific integrated circuits (ASICs) like TPUs, which can offer superior performance and energy efficiency compared to off-the-shelf GPUs.
Effective integration with cloud environments is essential for managing AI workloads across hybrid and multi-cloud setups, as platforms like VMware’s Cloud Foundation can assist in this integration, ensuring seamless deployment and management of AI resources.
