Scalable Processors with Built in AI Accelerators

Usually, when people hear the words artificial intelligence (AI) accelerator, they associate this with Graphics Processing Units (GPUs), which have undoubtedly changed the AI landscape by enabling the training of increasingly large models, making them accessible to broader audiences.?

However, several features built into Central Processing Units (CPUs) accelerate AI workloads by speeding up the processing of deep learning algorithms, which boosts AI workloads in training and inference, similar to that of a GPU.?

I’m Ronald van Loon, Intel partner (sponsored by Intel), and I reviewed with Intel how you can use CPUs through the entire AI pipeline, achieving significant performance and results, often before evaluating whether or not a separate, discrete GPU accelerator is needed.?

By integrating these features into their CPUs, Intel has enabled developers to train and run AI models on various hardware platforms, from desktop computers to data centers and edge devices.

These scalable processors allow organizations to build and deploy AI everywhere with optimized open-source frameworks and tools and use any AI code for every workload.?

To optimize AI pipelines, organizations can turn to 4th Gen Intel Xeon Scalable processors with Intel Advanced Matrix Extensions (Intel AMX), a built-in AI accelerator, for a high-performing, efficient, and cost-effective approach to accelerating AI workloads.

CPU Challenges and Competitive Differentiators

End-to-end AI pipelines involve building and deploying a complete machine learning system, from data acquisition to model training and deployment.?

While AI pipelines offer many benefits, like faster model iteration and more efficient use of computing resources, they also present several challenges that developers and data scientists must address.?

In fact, only 53% of AI projects make it from prototype to production for end-to-end AI pipelines.

Part of this is because of infrastructure challenges, as businesses have existing processors running various workloads and applications. It’s more efficient to develop upon existing CPU processors that your business is familiar with that are becoming increasingly capable for AI.??

According to Intel’s market modeling of the worldwide installed base of data center servers running AI Inference workloads, 70% of data center inferencing is run on Intel processors. For most customers, Xeon CPUs are sufficient in meeting AI Service Level Agreements (SLAs.) However, AI accelerators can further boost performance, enabling customers to handle more significant datasets, more complex models, and meet more demanding SLAs before considering the need for a discrete accelerator.

Infrastructure presents additional challenges for AI pipelines, like scalability. As the volume of data and complexity of models increases, a company’s infrastructure must be able to scale up to meet the demand. This includes scaling up compute and storage resources and ensuring that the infrastructure can handle the high throughput of data.

This directly impacts resource management because AI models require significant computational resources, including GPUs, CPUs, and memory. Efficiently managing these resources can be challenging, especially when running multiple models simultaneously.

Training AI models requires significant computational power and can be time-consuming. Therefore, it's crucial to optimize the infrastructure to ensure that model training is efficient and cost-effective.?

CPU AI accelerators are uniquely positioned to bring value because they contain deep learning accelerator performance built into every core. This results in significant performance increases for AI and deep learning inference and training workloads for popular advanced use cases like speech recognition, object detection, image segmentation, recommendation systems, natural language processing, and image classification.?

Intel AMX built-in accelerator delivers common applications faster through hardware acceleration, optimizing general computing and AI workloads. In addition, developers can code AI functionality or non-AI functionality as needed.

The 4th Gen Intel Xeon processor is equipped with a diverse range of accelerators, known as Intel Accelerator Engines, which are tailored to enhance performance for specific workloads. One such accelerator is the Intel Advanced Matrix Extension (Intel AMX), designed to facilitate performance improvements in the AI domain. Another example of an accelerator on the processor is the Intel Advanced Vector Extensions 512 (Intel AVX-512), which is also aimed at boosting performance.

Intel CPUs are optimized for industry standard frameworks such as toolkits and libraries like PyTorch and TensorFlow, and neural network libraries like the Intel? oneAPI Deep Neural Network Library (oneDNN).

Use Cases for CPU AI Accelerators?

Use cases are critical in demonstrating the relevance of CPU AI accelerators. These accelerators are designed to speed up the computation of AI workloads. Their effectiveness can only be evaluated through specific use cases that demonstrate the benefits of their acceleration capabilities in various scenarios.

Recommender systems: Provide a personalized end-user experience, whether recommending books or movies or displaying tailored advertisements. Develop a deep learning-based recommendation system that incorporates real-time user behavior signals and contextual features such as time and location, delivering results in near real-time.

Natural language processing (NLP): As the global market is expected to surpass 80.68 billion USD by 2026, NLP applications like language inferencing and machine learning have become indispensable for businesses seeking to support and expand various functions, such as chatbots, sentiment analysis, and machine translation.?

Retail e-commerce software solutions: Maximize revenue and enhance customer satisfaction by effectively reducing transaction time and handling peak demands through deep learning inference and training, along with AI-optimized frameworks such as PyTorch and TensorFlow.

Image recognition: A type of computer vision technology that enables machines to identify and classify objects within digital images or videos. With advancements in deep learning algorithms, image recognition has become more accurate and efficient for uses, including product recognition for inventory management and supply chain optimization and object detection for quality control and defect identification in manufacturing.

Machine/language translation: Machine or language translation is the process of converting text from one language into another to produce an accurate and understandable translation of a piece of text. Machine translation has many applications, including improving communication between people who speak different languages, and enabling cross-border commerce.

With CPU AI accelerators, organizations can process large amounts of data and run more complex algorithms. They can be easily integrated into businesses' existing infrastructure, reducing the need for additional hardware and software investments.?

Also, they can be easily scaled up or down to meet changing business needs, tailored to their AI solutions and unique requirements, and fully leverage any AI capabilities to drive innovation, improve efficiency, and enhance customer experiences.

CPU AI accelerators are integral for advanced business use cases because they provide improved performance, cost efficiency, scalability, customizability, and competitive advantage.

Accelerate Business Innovation with CPU AI Accelerators

CPU AI accelerators are an indispensable tool for businesses that seek to leverage the power of AI to drive innovation and growth.?

By processing vast amounts of data quickly and accurately, CPU AI accelerators allow businesses to make more informed decisions, generate valuable insights, and deliver personalized customer experiences.?

CPU AI accelerators are enabling businesses to innovate and transform their operations in ways that were once unimaginable. As the demand for AI-powered solutions continues to grow, the role of CPU AI accelerators in enhancing business innovation will only become more critical, providing a powerful tool for businesses to stay competitive and drive growth in the years to come.

Check out Intel for more information about AI accelerators built into CPUs.