As a critical technology in the digital and intelligent era, AI has become deeply integrated into our daily lives. From smartphones and autonomous vehicles to medical diagnostics and financial analysis, AI is ubiquitous. Behind all of this lies the technical support of AI chips.
AI chips, also known as artificial intelligence processors, are integrated circuits specifically designed and optimized to accelerate AI algorithms. They efficiently handle intelligent tasks such as computer vision, speech recognition, natural language processing, and traditional machine learning.
The emergence of AI chips addresses the inefficiency and high energy consumption of traditional processors when handling AI tasks. Unlike conventional central processing units (CPUs) and graphics processing units (GPUs), AI chips are specially optimized for AI algorithms and computational tasks, enabling high-performance processing of complex operations like deep learning and machine learning.
As AI technology rapidly advances, traditional CPUs and GPUs increasingly struggle with large-scale AI workloads. AI chips employ techniques such as parallel computing, low-precision arithmetic, and dedicated instruction sets to significantly enhance the efficiency and performance of AI computations. For example, Google’s TPU (Tensor Processing Unit) outperforms traditional CPUs and GPUs in deep learning tasks, establishing itself as a landmark product in the AI chip domain.
1. Classification of AI Chips
① DPU (Data Processing Unit)
A DPU is a specialized processor for data centers, regarded as the "third pillar" alongside CPUs and GPUs. By optimizing data processing efficiency, offloading CPU workloads, and enhancing security, it has become critical infrastructure for cloud computing and AI applications.
Technical Features
· High Speed, High Bandwidth, Low Power Consumption
DPUs enable fast, high-throughput, and energy-efficient data transmission and processing.
· Workload Offloading
DPUs offload network, storage, and virtualization tasks from CPUs, freeing up computational resources and boosting data center performance.
· Resource Liberation
By minimizing CPU involvement in data handling, DPUs ensure direct data transfer to applications, GPUs, or storage devices, avoiding bottlenecks and CPU overload.
· Scalability
DPUs eliminate network bottlenecks in distributed systems, reducing communication latency and enhancing cluster-wide computational capacity.
· Security
DPUs implement hardware-level encryption/decryption algorithms and isolate tenant networks, providing robust security.
Applications
· AI
Supports optical neural networks, optical computing, and quantum computing.
· Cloud Networking
Accelerates network functions, storage interfaces, and service mesh offloading.
· Storage
Manages storage resources with hardware-level QoS for bare-metal, virtualized, and containerized environments.
· Security
Enables secure boot, encrypted data transmission, and tenant-specific firewall rules.
② NPU (Neural Processing Unit)
An NPU is a processor designed to accelerate AI and machine learning tasks by optimizing parallel computing and energy efficiency for neural network operations. It mimics the brain’s neural structure using massively parallel processing units and efficient interconnects.
Technical Features
· Massive Parallelism
Multi-core architecture supports simultaneous data streams and accelerates matrix multiplication, convolution, and activation functions.
· High Energy Efficiency
NPUs consume only 10%-20% of the power of CPUs/GPUs, with up to 5x higher efficiency for specific tasks.
· Hardware Acceleration
Built-in tensor cores and quantization modules enable INT8/FP16 mixed-precision computing, boosting inference speeds by 3-10x.
Applications
· Mobile Devices
Powers computational photography (e.g., portrait mode, night photography).
· Autonomous Driving
Processes multi-camera data fusion for real-time lane detection and obstacle avoidance.
· Cloud Computing
Accelerates NLP services to handle tens of thousands of queries per second.
③ GPU (Graphics Processing Unit)
GPUs are mature general-purpose AI chips originally designed for graphics and gaming. Compared to CPUs, GPUs feature more cores and higher memory bandwidth, excelling in computational efficiency and rendering.
NVIDIA’s A100 and H100 GPUs dominate AI training and inference with their unparalleled compute power. Modern GPUs have evolved from graphics rendering to supporting deep learning, making them the preferred accelerators for AI servers.
Categories
· Professional GPUs
Used in engineering, scientific research, and medical fields (e.g., NVIDIA Quadro, AMD Radeon Pro).
· Consumer GPUs
Target gaming and home users (e.g., NVIDIA GeForce, AMD Radeon, Intel Arc).
④ FPGA (Field-Programmable Gate Array)
FPGAs offer unmatched flexibility compared to ASICs and GPUs. They execute entire algorithms on-chip without relying on DRAM, combining software programmability with hardware parallelism and low latency.
· Advantages
Rapid iteration, cost-effectiveness, and adaptability make FPGAs ideal for AI, 5G, and other fast-evolving fields.
⑤ ASIC (Application-Specific Integrated Circuit)
ASICs are fully customized chips optimized for specific applications, offering ultra-low power consumption and high efficiency. They are widely used in smartphones, security cameras, smart homes, and drones.
· Examples
Google’s TPU, Intel’s VPU, and autonomous driving-focused BPUs.
2. Working Principles of AI Chips
AI chips rely on specialized architectures and optimizations for AI algorithms:
①Parallel Computing Architectures like GPUs, TPUs, and NPUs integrate thousands of cores to process massive datasets in parallel.
② Hardware Acceleration
Techniques like SIMD (Single Instruction, Multiple Data) and dedicated accelerators speed up matrix operations (e.g., convolution, pooling).
① Energy Efficiency
Low-precision computing (e.g., INT8) and advanced memory technologies reduce power consumption while maintaining accuracy.
② Distributed Architecture
Modern AI chips, such as Google’s TPU, distribute tasks across tensor cores to achieve trillions of operations per second.
③ Software-Hardware Co-Design
Frameworks like TensorFlow and PyTorch are optimized for specific chips (e.g., TPU-TensorFlow integration).
3. Key AI Chip Manufacturers
① NVIDIA
· Flagship Products: A100, H100 (Hopper architecture, 4nm process, FP16/FP8 support).
· Applications: Supercomputing, cloud services, and large language model training.
② Intel
· Flagship Products: Xeon processors, FPGAs, Nervana NPUs.
· Expansion: Acquired Mobileye for autonomous driving AI solutions.
③ AMD
· Flagship Products: EPYC-based AI solutions, Instinct MI300 series (Chiplet design).
· Strengths: High performance and energy efficiency.
④ Google
· Flagship Product: TPU (Tensor Processing Unit), optimized for TensorFlow and deployed in data centers.
⑤ Qualcomm
· Flagship Products: Snapdragon chips with Adreno GPUs and Hexagon DSPs.
· Applications: Smartphones, IoT, and automotive AI.
AI chips are the core enablers of the AI revolution, offering unmatched performance and efficiency across industries. From NVIDIA’s GPUs to Google’s TPUs and Huawei’s Ascend series, these chips continue to evolve, driving innovations in computing power, energy efficiency, and application diversity. As AI models grow in scale and complexity, advancements in chiplet technology, photonic computing, and quantum acceleration will further redefine the boundaries of intelligent systems.
