Introduction to AI Chips
Introduction to AI Chips
Artificial intelligence requires high memory bandwidth and low latency, with "computing power" becoming the key factor in its practical implementation. Different types of processors play distinct roles, ranging from general-purpose CPUs to parallel-optimized GPUs, NPUs designed for smart devices, and TPUs tailored for training massive models. Each excels in its respective domain, collectively forming a heterogeneous AI system that ensures efficient execution of AI tasks.
What is CPU？
The full name of CPU is "Central Processing Unit", which is the "brain" of computing devices. It excels at handling various types of software instructions, controlling system flow, executing logical operations, and scheduling other hardware.
Advantages: Strong universality, capable of executing any model or task (including AI models); Low development threshold; Ecological maturity.
Limitations: Not as efficient as dedicated hardware in deep learning training or large-scale parallel computing. The reasons include a small number of cores, low parallelism, and limited memory bandwidth.
Applicable scenarios: Traditional machine learning (such as scikit learn, XGBoost), small-scale model prototype development, small batch inference, lightweight tasks.
In short, if you are only doing very lightweight tasks such as debugging, exploration, or deployment, CPU is a reliable but not the fastest choice. Today's CPUs mainly come from the following manufacturers: Intel: products include Core series (consumer grade), Xeon (server/workstation), Pentium, and Celeron (entry-level) chips;
AMD: Provides Ryzen (consumer/high-performance) and EPYC (server) processors, as well as APU (Accelerated Processing Unit), which integrates CPU and GPU on the same chip.
What is GPU?
GPU, also known as Graphics Processing Unit, is optimized for high-throughput large-scale parallel data processing. Originally designed for image rendering, it has become an important tool for deep learning training and inference in recent years. Its key features are: having hundreds or thousands of parallel computing cores, proficient in matrix operations and large-scale parallel loading, very suitable for convolutional neural networks (CNN), recurrent neural networks (RNN), Transformers, etc.
Advantages: high parallelism, large bandwidth, high throughput; Most mainstream AI frameworks, such as TensorFlow and PyTorch, have good support for GPUs.
Limitations: Although proficient in deep learning, its versatility is still lower than that of CPU; Higher power consumption and heat dissipation requirements; It may not be suitable in edge devices/low-power scenarios.
Applicable scenarios: Training and running large-scale deep learning models, large-scale data processing, visual/speech tasks, cloud/server-side inference and training.
If you are building an AI system that requires a larger model and higher throughput, GPU is usually the "main ship".
Application: NVIDIA, the global leader in GPUs, has built a complete parallel computing platform CUDA(Compute Unified Device Architecture）， Releasing GPU hardware capabilities into the field of general computing significantly reduces the threshold for GPU programming.
What is NPU?
NPU (Neural Processing Unit) is a type of chip specifically designed for neural network inference, especially real-time inference on edge devices. They are commonly found in scenarios such as smartphones, smart IoT devices, and smart cameras.
Advantages: Specialization, high energy efficiency, low power consumption, and adaptability to real-time/local inference scenarios (such as face unlocking, voice assistants, translation functions). Limitations: Mainly used for inference (rather than large-scale training); Low universality and may not support all model structures; The design goal tends to be 'task specific'.
Applicable scenarios: Lightweight neural networks on mobile/IoT devices (such as MobileNet, Tiny BERT, etc.), offline real-time AI capabilities, applications sensitive to power consumption or latency.
If your deployment environment is on mobile phones, embedded devices, or offline scenarios, NPU is often the best choice. Applications: Apple (Neural Engine), Qualcomm (Hexagon DSP), Huawei (Kirin NPU)
What is TPU?
TPU (Tensor Processing Unit) is a specialized AI accelerator developed by Google LLC, designed for large-scale deep learning training and inference, especially for optimizing tensor operations (tensor=multidimensional array).
Advantages: extremely high matrix processing throughput, optimized for large models such as BERT and GPT series, and excellent performance in cloud/data center environments.
Limitations: Mainly targeting the TensorFlow ecosystem (although supporting interfaces from other frameworks in extensions); The deployment environment is more concentrated on cloud/large-scale servers rather than mobile devices.
Applicable scenarios: Training huge neural networks (with millions to billions of parameters), cloud inference services, big data AI tasks, and scenarios that require extremely high throughput and performance. In short, when you need to train "big models" or run "large-scale online inference services," TPU is a high-performance but more demanding option.
Applications: Google Ironwood, Zhonghao Xinying
What is DPU?
DPU (Data Processing Unit) is a processor dedicated to data center and network tasks, used for network, storage, data mobility, and AI infrastructure acceleration. Focus on offloading data related and network tasks, freeing up CPU/GPU computing power to concentrate on processing core AI computing. DPU supports lightweight model inference, such as implementing real-time object detection in intelligent cameras. Optimize cross node data transmission through RDMA technology to improve distributed computing efficiency. Applications: NVIDIA (BlueField), Intel, Fungible
What is APU?
APU (Accelerated Processing Unit), AMD has developed a hybrid processing unit architecture that integrates the capabilities of CPU and GPU into a single chip package, giving birth to the Accelerated Processing Unit (APU). This design eliminates the performance bottleneck caused by transferring data back and forth between independent processors. The biggest representative of this concept is the AMD Instinct MI300A. It integrates 24 "Zen 4" CPU cores, 228 GPU computing units, and up to 128GB of HBM3 memory. The memory of MI300A can be shared between CPU and GPU, with a peak bandwidth of 5.3TB/s. Its multi chip architecture stacks chiplets and bare chips, placing CPU and GPU computing units adjacent to high bandwidth memory, and interconnected by AMD's Infinity Fabric and Infinity Cache. In addition, the chip fully supports mainstream AI data formats and has hardware level sparsity acceleration capability.
What is an iPhone?
IPU (Intelligence Processing Unit) is a specialized hardware accelerator used for efficiently executing artificial intelligence (AI) and machine learning (ML) tasks. IPU is designed to meet the needs of modern AI algorithms, with highly parallel computing capabilities and powerful reasoning abilities. Compared to traditional general-purpose processors, IPUs are able to process large-scale data more efficiently and perform complex pattern recognition and inference tasks. IPU has been widely used in various fields of AI applications, such as computer vision, natural language processing, speech recognition, etc. They can accelerate tasks such as model training, data analysis, pattern recognition, and inference, providing users with faster and more accurate results.
The GPU developed by Graphcore is a large-scale parallel processor with 1472 independent processor cores, capable of running nearly 9000 parallel threads simultaneously and tightly coupled with 900MB of high-speed on-chip memory. This means that data can be processed directly at the storage location.
