Dedicated systems to accelerate machine learning applications are helping engineers accomplish more with AI.
In an era marked by data explosion and rapid technological advancements, artificial intelligence (AI) has emerged as a transformative force, revolutionizing industries across the globe. With AI’s potential to unlock valuable insights and drive efficiency, businesses are increasingly investing in AI infrastructure to harness its power.
“Enabling the use of generative AI and delivering cognitive decisions at scale is having a transformational impact throughout remote locations across financial, manufacturing, healthcare, retail and smart city applications,” said Robert Daigle, Lenovo’s business head for AI, in an interview with engineering.com.
As AI becomes an integral part of various industries, the demand for affordable and scalable AI infrastructure has increased. Hardware OEMs are addressing this demand by developing targeted solutions.
The AI hardware landscape
“Data scientists and AI practitioners spend over 77% of their time with data pre-prep, training, modelling and tuning tasks,” explains Daigle. “This workflow has a huge demand for powerful processors (core count and clock speed), memory and graphical horsepower.”
Hardware makers like Lenovo, Dell and HP are offering AI-focused systems optimized to handle the intensive computational requirements of AI workloads and enable engineers and data scientists to extract valuable insights from vast datasets. These leverage powerful CPUs and GPUs for AI inference, enabling efficient real-time decision-making, and include powerful servers and high-performance storage systems.
The AI hardware landscape is not limited to traditional OEMs. Cloud service providers, such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud have developed their own AI-focused hardware solutions. For example, AWS has developed Inferentia, an AI inference chip designed to deliver high performance and cost-effective inference for machine learning models, while Google’s Tensor Processing Units (TPUs) are custom-designed AI accelerators developed for both inference and training tasks.
AI-enabled storage
AI-enabled storage systems are specifically designed and optimized to handle the unique requirements of AI workloads, which often involve massive datasets, high-throughput data access and complex data processing. These storage systems aim to provide the necessary performance, scalability and reliability to meet the demands of AI applications. AI-enabled storage systems are optimized for AI workloads in the following ways:
⦁ Data throughput and bandwidth: AI workloads typically involve large datasets that need to be processed quickly. AI storage systems are designed with high data throughput and bandwidth capabilities to efficiently transfer data between storage devices and AI processing units (such as GPUs or TPUs). This ensures that data can be fed to the AI models at a rate that maximizes computational efficiency.
⦁ Low latency: Reducing data access latency is crucial for AI applications, especially in real-time or interactive scenarios. AI storage systems are optimized to minimize read and write latencies to enable fast data retrieval and storage.
⦁ Scalability: AI projects often require massive amounts of data storage, and the storage system must be able to scale easily to accommodate growing datasets. AI-enabled storage solutions are designed to scale both in terms of capacity and performance to meet increasing demands.
⦁ Parallelism: AI workloads can be highly parallelizable, and storage systems optimized for AI take advantage of this characteristic. They are designed to support concurrent data access from multiple AI processing units, allowing for efficient parallel processing of AI algorithms.
⦁ Data management and tiering: AI storage systems may employ intelligent data management techniques and tiering strategies. Frequently accessed data or data needed for immediate processing can be stored on high-performance storage tiers, while less frequently used data may reside on lower-cost, high-capacity tiers. This tiering approach helps balance performance and cost-effectiveness.
⦁ Data reduction and compression: AI models often require vast amounts of data, and storage systems may use data reduction and compression techniques to minimize storage footprint while preserving data integrity. These techniques can save storage space and potentially improve data access speeds.
⦁ AI-aware data placement: Some AI-enabled storage systems use AI algorithms to analyze data access patterns and intelligently place data closer to the processing units that need it most. This can reduce data movement across the storage infrastructure and further improve performance.
⦁ Data security and privacy: AI workloads often deal with sensitive data, so AI storage systems prioritize data security and privacy measures. These may include encryption, access controls, and compliance with relevant data protection regulations.
⦁ Integration with AI frameworks: AI storage solutions may offer integrations with popular AI frameworks and libraries, making it easier for data scientists and AI developers to work seamlessly with the storage infrastructure.
Investing in AI Infrastructure
AI infrastructure empowers engineers and data scientists to process and analyze data at unprecedented speeds, enabling timely decision-making and driving innovation. The integration of AI infrastructure into an organization’s technology stack requires careful consideration and planning, particularly in terms of budgeting.
As AI becomes an integral part of business operations, companies need to reassess their hardware budgeting strategies to accommodate their AI needs. While AI hardware may involve higher upfront costs compared to traditional computing infrastructure, the long-term benefits can outweigh the initial investment. By improving operational efficiency, accelerating time-to-insight and driving innovation, AI infrastructure investments can lead to significant cost savings and revenue growth over time.
The exponential growth of data and the transformative potential of AI necessitate a comprehensive investment in AI infrastructure. “More optionality from compute platforms along with an ever-expanding ecosystem of GPU and accelerator vendors will help keep AI accessible and affordable for organizations of all sizes,” says Daigle.
