What is Intel Deep Learning Boost: Pros and Cons

What is Intel Deep Learning Boost: Pros and Cons

Intel Deep Learning Boost is a proprietary technology and feature from Intel for improving the performance of machine learning and deep learning tasks such as model training and inference processes. It was first introduced in 2019 via the Cascade Lake architecture and was featured in the second-generation Intel Xeon Scalable processors. It is also now featured in selected Intel Core processors and one of the main features of all Intel Core Ultra processors either through the central processing unit, the integrated graphics processing unit, or both. This article explains the advantages and disadvantages or pros and cons of Intel Deep Learning Boost.

Pros of Intel Deep Learning Boost: Features and Applications

The increasing practical applications of artificial intelligence in the realms of personal computing and workstation computing have pushed semiconductor manufacturers to design processors with native capabilities for processing AI workloads. This also has resulted in the emergence of the AI PC or a new class of personal computers with built-in hardware AI capabilities.

Intel Deep Learning Boost represents a solution for optimizing artificial intelligence workloads on a personal computer. It is specifically a suite of instruction set architecture features designed as part of the architecture of an Intel processor. This means that it includes specific instructions geared toward handling mathematic operations commonly used in deep learning.

The instructions include Advanced Vector Extensions 512 or AVX-512 which provide wider vector registers for improved processing of large data sets. The more specific AVX-512 Vector Neural Network Instructions combine multiple machine instructions used in previous generations into one and AVX-512 BF16 for processing bfloat16 floating-point number format.

Nevertheless, through Intel Deep Learning Boost, an equipped Intel processor is optimized for training deep learning models and processing model inferences. It has been integrated on either the main CPU or several models of an Intel Iris Xe or Intel Arc integrated graphics processors used in Intel Core Ultra. The following are its specific advantages and applications:

1. Improved Performance

The main advantage of Intel Deep Learning Boost is that it improves further the performance of a particular personal computer. The applications of artificial intelligence in various modern PC use cases have called for special attention in AI workload processing. Notable examples include image recognition and natural language processing features found in different software programs or various automated processes in modern operating systems.

2. Wide Framework Support

It supports various deep learning techniques and architectures such as Vector Neural Network Instructions for accelerating algorithms based on convolutional neural networks and common frameworks or tools such as TensorFlow, PyTorch, and Intel Math Kernel Library for Deep Neural Networks. This technology also enables faster and more efficient computation of 8-bit and 16-bit data which are used in several deep learning applications.

3. Resource Efficiencies

Another advantage of Intel Deep Learning Boost has reduced the cost of computation involved in deep learning tasks compared to previous CPUs and GPUs. Its integration in the architecture of the main CPU or GPU has also eliminated the need for a separate AI accelerator or an AI engine in most AI applications. This technology also reduces memory usage and optimizes cache usage by using a single instruction for deep learning computation.

Cons of Intel Deep Learning Boos: Limitations and Drawbacks

Intel Deep Learning Boost is not a separate or hardware-based artificial intelligence accelerator. This means that it can still put a strain on the involved CPU or GPU when it comes to processing specific deep learning workloads despite the performance and efficiency gains. Having a separate AI accelerator eliminates this strain from the main processor or graphics processor.

Take note that Intel has launched a dedicated AI accelerator called the Intel AI Boost alongside the introduction of Intel Core Ultra. Other companies also have a separate hardware component for accelerating AI workloads. Some notable examples include the Neural Engine found in Apple chips and the XDNA AI engine found in newer AMD Ryzen computer processors.

It is also worth mentioning that the performance and capabilities of a discrete graphics processor or a true separate AI accelerator such as a tensor processing unit remain unmatched when it comes to training a large artificial intelligence model or intensive artificial intelligence model inferencing. Below are the specific disadvantages of Intel Deep Learning Boost:

1. Limited Compatibility

The algorithms and frameworks or tools that are supported by Intel Deep Learning Boost are not universal. Vector Neural Network Instructions or VNNI require specific software libraries to be optimized for maximum benefits. Not all deep learning frameworks and tools integrate fully with this technology. It also does not provide methods for explaining the logic or reasoning behind the decisions or predictions of a particular deep learning model.

2. Introduces Inefficiencies

Remember that this technology can strain the involved CPU or GPU. This comes from the fact that the processing of deep learning workloads still transpires in a particular processor that also has to handle other workloads. This can translate to higher power consumption, special cooling requirements, and overall performance inefficiencies. It also consumes other resources critical to overall operations. Examples include memory and system cache.

3. Alternative Solutions

An integrated solution for supporting and accelerating AI workloads in personal computers is not the best route. The applications of AI are evolving and becoming more complex. A dedicated AI accelerator like a discrete GPU, a component that is part of a system-on-a-chip, a separate neural engine or tensor processing unit, or a vision processing unit still offers superior performance for workloads that are reliant on complex models and massive datasets.

Takeaways: Pros and Cons of Intel Deep Learning Boost

Intel Deep Learning Boost is a solution for enabling a computer processor to handle AI or more specific deep learning workloads. It eliminates the need for a separate component or processor and uses the available resources of an involved CPU or GPU. However, because of the ongoing AI revolution, specific AI applications are becoming more complex. It is possible that integrated solutions such as Intel Deep Learning Boost will become less relevant in the future. Having this technology still has notable advantages and a computer can perform better if it has a dedicated hardware for AI acceleration. This has been seen in newer computer processors such as the Intel Core Ultra, the Apple M series of chips, and the AMD Ryzen 7040 series.