With the rapid growth of Artificial Intelligence (AI), particularly in the fields of deep learning (DL) and large language models (LLMs), enterprises are transforming their computing infrastructure. Traditional CPUs, even with multiple cores, threads, and gigahertz (GHz) processing speeds, are falling behind in handling the high-volume, parallelized data processing required by AI workloads. That is where intelligent and high-end processing cores come into the picture. Robust processors such as Neural Processing Units (NPUs), Intelligence Processing Units (IPUs), Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs) have emerged as specialized solutions tailored for AI acceleration.
This article provides a comprehensive guide on what type of processors these AI systems use. We will also dive into how these processors handle computationally intense and parallel AI model computations for faster training and inference. From the need for these processors to real-world use cases to each processor’s challenges, this article will provide a 360-degree view of intelligent processing power.
AI Workloads across Enterprises
The ever-growing demand for AI is driving a monumental shift. Enterprises are adopting generative AI and large language models (LLMs), building inference systems, and developing data-intensive applications and services to handle high-volume workloads. Handling Big Data solutions is possible with DPUs. Intelligent processing and task scheduling are possible with IPUs. For training large models and AI algorithms, GPUs and TPUs are popular.
Lastly, for creating artificial neural networks for AI inferences and deep learning models, NPUs are popular. Each of these processors has a specifically designed architecture that tackles specific challenges in processing, data movement, parallelization, efficiency, cloud processing services, scalability, and real-time inference. For various ideal scenarios across enterprise workloads, they leverage high-performance computation systems with varied computational proficiency. Accelerating the AI application creation or model development requires these processing systems for specific situations. Suitable processing units can lead an organization toward the AI revolution.
List of Processors used for AI System Development
Enterprises are using various types of processors to process and manage data-intensive workloads. Let us explore these processing units and their potential one by one.
1. GPUs
A GPU (Graphics Processing Unit) is a parallel processor designed originally to accelerate rendering of images and video but is now widely used for parallel computation tasks such as deep learning. A Graphics Processing Unit (GPU) uses specialized processors designed to rapidly handle complex computations. With hundreds or thousands of processing cores, GPUs excel at parallel processing, enabling them to tackle demanding computational tasks more efficiently than traditional CPUs.
Originally developed for graphics rendering, GPUs powered 2D/3D imaging, gaming, video production, and animation. However, their capabilities have expanded far beyond visual applications. Modern enterprises and researchers now leverage GPUs for:
- Big data analytics
- Machine learning and deep learning model training
- Data mining operations
- Neural network development
- Other compute-intensive workloads
The GPU’s ability to maintain high performance under substantial processing demands makes it ideal for multitasking and data-heavy operations across these advanced computing domains.
How does parallelization take place in GPUs?
GPU is composed of thousands of small cores that have the potential to execute simultaneously. It utilizes the Single Instruction, Multiple Thread (SIMT) architecture, which makes GPUs ideal for AI and ML workloads that use matrix multiplications. Apart from the SIMT model, it utilizes a global memory that shares data across all threads. Furthermore, it allows shared memory, offering intercommunication within the same block. GPU also delivers optimized memory access patterns to minimize latency for bulk data transfer. Parallelization is also possible because the CUDA (offered by NVIDIA) and OpenCL allow developers to offload computation-heavy operations to the GPU effectively.
2. TPUs
TPUs are custom-designed AI accelerators developed by Google. These Application-Specific Integrated Circuits (ASICs) help AI/ML engineers to accelerate machine learning workloads, particularly those based on Google’s TensorFlow framework. TPU architecture is tailored for tensor operations, where ‘tensor’ means an array of powerful ASIC processing systems connected in a cluster. TPUs contain massive matrix multiplication units (MXU) capable of performing tens of thousands of multiply-and-accumulate (MAC) operations per cycle to boost ML workloads.
These are the backbone of deep learning models. There are two types of TPUs available in the market: Edge TPUs and cloud TPUs. Cloud TPUs are for large-scale training (e.g., TPU v4 Pods) while edge TPUs are for on-device inference with ultra-low power.
3. NPUs
Neural Processing Units are highly specialized processing chips designed to boost neural networks and artificial neural working. These are on-device inference processors that accelerate ANN algorithms. Engineers can integrate them with portable devices, System on a Chip (SoCs) in mobile phones, IoT devices, and edge systems.
How does NPUs work?
Engineers designed NPU to mimic the human brain’s functionality and structure. It consists of interconnected processing units called neurons, which enable parallel data processing in an ANN. This parallel processing capability is crucial for tasks like real-time speech recognition, image analysis, and natural language processing. Most NPUs fuse multiple layers (e.g., convolution, ReLU, and pooling) into single-operation pipelines to minimize memory transfers and improve latency.
NPU also has parallel engine blocks that can handle complex operations such as dot product, vector addition, and normalization. Furthermore, many weights in modern neural nets are zero. NPUs help optimize artificial neural operations by skipping these calculations entirely, improving efficiency. To accelerate ANN operations, NPUs also have dedicated caches and buffers that bypass external DRAM access, reducing power consumption.
4. IPUs
An Intelligence Processing Unit (IPU) is a specialized microprocessor designed by Graphcore. They accelerate machine learning (ML) workloads, particularly those used in large model development and predictive AI projects. IPUs are distinct from traditional GPUs because they offer a different path to deliver parallel processing. Engineers have tailored it to the unique demands of AI and machine learning algorithms. It comes with thousands of independent processing units that can multitask to deliver fine-grained parallelism. It can handle small computing tasks with minimal overhead.
Benefits and Drawbacks of Different Intelligent Processors
The process of binding services from independent vendors including AWS for compute strength and Snowflake for storage and Hugging Face for training creates complex interconnected systems that AI developers must maintain. The diverse system components create delays that hinder both development and testing periods.
Inefficient scheduling
The scheduling process in Kubernetes becomes inefficient because it does not properly distribute GPU resources to support parallel execution of AI tasks. AI programming requires special orchestration systems to make GPUs operate more efficiently.
The need for an AI-optimized cloud architecture
Let us now dig into the various advantages and disadvantages of different processors when dealing with AI/ML workloads.
Advantages of GPUs:
GPUs offer thousands of powerful cores ideal for matrix computations for parallel processing.
GPU companies provide rich collection of libraries and languages such as CUDA, cuBLAS, cuDNN, and TensorRT to exploit the ever-increasing potential GPU architecture for faster linear algebra used in deep learning.
With GPUs, the training of CNNs and transformers becomes dramatically faster since all its cores become dedicated to processing such operations.
GPUs are scalable when used with cloud technology. These power-hungry processors complete tasks faster, making them more energy-efficient per computation.
Disadvantages of GPUs
High-end GPUs (such as NVIDIA A100, H100, and RTX 4090) are too costly and can significantly increase AI project development and infrastructure expenditures.
GPU has less internal memory (usually 24 to 80 GBs). It often limits training LLMs and extensive generative AI projects.
GPU-based programming requires specialized knowledge and training to utilize its architecture and cores for parallel processing in model development.
Advantages of TPUs
TPUs are ideal for tensor operations and high-end workloads. TPUs help optimize deep learning workloads while working with TensorFlow.
Since TPUs are a collection of GPUs but operate much faster than high-end GPUs, they are ideal for matrix multiplications and convolution operations.
TPUs are not just for training; Edge TPUs are lightweight and ideal for low-latency inference on edge devices.
TPUs are available as a Google Cloud Platform (GCP) service, so you don’t need to invest in hardware to use TPUs.
Disadvantages of TPUs
TPUs have limited framework support. They are ideal for working with the Tensorflow library.
Operational efficiency degrades with inferior performance when AI/ML engineers execute tasks other than tensor algebra or heavy non-ML tasks.
AI engineers should be exclusively required to understand XLAs (Accelerated Linear Algebra) compilers and operations.
TPU debugging tools are less mature than GPU debugging or profiling tools like NVIDIA Nsight, which makes fine-tuning intricate.
TPUs have limited framework support. They are ideal for working with the Tensorflow library.
Operational efficiency degrades with inferior performance when AI/ML engineers execute tasks other than tensor algebra or heavy non-ML tasks.
AI engineers should be exclusively required to understand XLAs (Accelerated Linear Algebra) compilers and operations.
TPU debugging tools are less mature than GPU debugging or profiling tools like NVIDIA Nsight, which makes fine-tuning intricate.
Advantages of NPUs
NPUs are perfect for real-time AI/ML projects such as speech recognition, face recognition, emotional intelligence detection, object identification, and edge AI computing.
NPUs consume less power than GPUs or CPUs while delivering high AI performance. Thus, they are perfect for mobile, embedded, and IoT devices.
Engineers have designed NPUs for on-device AI projects. NPUs are compact, and we can use them on embedded systems and in SoCs.
Some NPUs support quantized inference (e.g., INT8 or FP16). They speed up model execution and reduce memory usage.
Advantages of NPUs
NPUs are perfect for real-time AI/ML projects such as speech recognition, face recognition, emotional intelligence detection, object identification, and edge AI computing.
NPUs consume less power than GPUs or CPUs while delivering high AI performance. Thus, they are perfect for mobile, embedded, and IoT devices.
Engineers have designed NPUs for on-device AI projects. NPUs are compact, and we can use them on embedded systems and in SoCs.
Some NPUs support quantized inference (e.g., INT8 or FP16). They speed up model execution and reduce memory usage.
Disadvantages of NPUs
NPU toolchains are often proprietary and lead to vendor lock-in. Thus, AI/ML models and deployments become platform-dependent.
Some NPUs only endorse low-precision arithmetic (INT8, FP16). It often reduces model accuracy if not properly quantized.
NPUs are commonly used for inference and not AI/ML training. Most AI/ML models are trained on GPUs and TPUs and only deployed to systems having NPU.
Since NPUs are special-purpose processing units (used for processing artificial neural networks), they aren’t suitable for general-purpose computing and logic-heavy modeling.
Advantages of IPUs
Fine-grained computation is possible with IPUs. They offer massively parallel computations across thousands of small independent cores. These processors are ideal for complex and dynamic AI workloads.
Since AI models like transformers, GNNs, and RNNs follow a non-linear execution flow, IPU graph processing architecture can make such AI workflows seamless.
IPUs offer concurrent operations with minimal latency, supporting real-time AI processing and rapid model inference.
For handling sparse computations and control-heavy modeling, IPUs are better than GPUs and TPUs.
Disadvantages of IPUs
The ecosystem is still maturing. That is because IPUs come with fewer tools, community support, and libraries.
AI engineers and developers need custom APIs and rewrite codes multiple times for efficient IPU utilization.
Conclusion
We hope this article provided a crisp idea on intelligent AI-supportive processors. The evolution of intelligent AI-supportive processors is remarkable. GPUs, TPUs, NPUs, and IPUs mark a significant leap in AI computation, enabling faster, more efficient, and scalable AI solutions. Each processor discussed in this article comes with unique strengths and ideal use-case situations. Some are advantageous when cooking AI models, while others are effective in serving these models to edge devices. From high-end model training in cloud environments to real-time inference on edge devices, enterprises are leveraging these processors across all AI developments. In the future, we will witness the combined use of all these specialized processors to build AI systems in-house and on cloud-based services. Contact us or Visit us for a closer look at how VE3’s AI solutions & Cloud can drive your organization’s success. Let’s shape the future together.
