What are AI chips and why are they important

Artificial Intelligence (AI) has undergone rapid advancements in recent years. This progress has led to unique computational requirements for modern AI systems, which traditional CPUs and GPUs cannot fully address. Initially, GPUs offered a solution for running AI models. However, as algorithms and applications have grown more complex, particularly in areas like deep neural networks, GPUs have shown limitations in efficiency and scalability.

This challenge stems from the fundamental architecture of general-purpose processors like CPUs and GPUs, which rely on sequential computing based on Von Neumann’s designs. These architectures are not optimized for the parallel processing and high-speed calculations that AI workloads require. To address these gaps, the development of AI-specific chips has become critical. These chips are purpose-built to handle tasks with greater efficiency, lower power consumption, and tailored performance capabilities.

Although semiconductor technology has advanced to the point where transistors in modern chips are only a few atoms wide, increased transistor density alone isn’t enough to execute sophisticated AI calculations effectively. The problem lies in how general-purpose chips like CPUs and GPUs perform calculations.

Traditional sequential computing, based on Von Neumann architectures, is incompatible with the demands of AI models and algorithms. As a result, the industry is now focusing on fundamental changes to hardware, giving rise to AI chips with entirely new architectures. These chips are designed to execute AI applications cost-effectively, efficiently, and with high performance.

What are AI chips?

AI chips are integrated circuits designed to accelerate AI tasks, particularly large-scale matrix operations and parallel processing. These chips are optimized for the unique demands of AI algorithms, enabling faster processing and reduced power consumption compared to traditional CPUs. They’re also tailored to handle the complexity of AI workloads, from machine learning and deep learning to computer vision, natural language processing, and autonomous vehicles.

As technology advances, AI chips will increasingly be crucial in driving new and innovative applications.

What makes AI chips differenet?

The limitations of traditional CPUs and GPUs in implementing AI tasks led to the development of AI chips. Much like GPUs were initially designed for 3D modeling, AI processing units (AI PUs) were developed explicitly for AI workloads.

Here are the key differences between AI chips and traditional processors:

Parallel computing: Unlike CPUs, which use sequential processing based on Von Neumann architecture, AI chips are designed for parallel processing. They execute multiple calculations simultaneously, leveraging the parallel nature of AI algorithms to improve processing speed and efficiency.
Low-precision arithmetic: AI chips rely on low-precision arithmetic, requiring fewer transistors for calculations. In contrast, CPUs are designed for high-precision arithmetic, and GPUs primarily process floating-point values, which are unnecessary for most AI calculations.
Smaller transistors: AI chips incorporate smaller transistors and fewer of them for calculations, thanks to low-precision arithmetic and parallel computing. This enables them to execute AI tasks faster and more efficiently than CPUs.
Lower power consumption: The smaller, fewer transistors in AI chips significantly reduce energy consumption, making them efficient for AI tasks and environmentally sustainable. AI chips are now viable for energy-constrained edge devices and have reduced the carbon footprint of data centers.
Customized design: AI chips are purpose-built for specific AI tasks, such as neural network processing, natural language processing (NLP), and image processing. Their tailored design ensures precision and accuracy, which is vital for applications like medical imaging and autonomous vehicles.
Flexibility: AI accelerator chips, such as application-specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs), are designed for dedicated AI tasks. FPGAs are often used for inference, while ASICs may handle training and inference. This flexibility allows AI chips to adapt to evolving algorithms, datasets, and processing demands.
Cost-effectiveness: Modern AI chips are more cost-effective than traditional processors. Even state-of-the-art AI chips are significantly cheaper and faster than those developed just a few years ago. This cost efficiency makes them indispensable for developing and deploying advanced AI algorithms.

Currently, AI chips are leading to greater generative AI, machine learning, and neural network processing breakthroughs.

The unique features of AI chips compared to CPUs and GPUs are summarized in the following table:

Why are AI chips useful?

AI chips have a specialized architecture compared to CPUs and GPUs. They include hardware units like tensor processing units (TPUs) or matrix multiplication units, which efficiently handle the mathematical operations required for AI workloads.

These chips feature built-in architectures optimized for parallel computing over low-precision arithmetic operations. They also often include large amounts of on-chip memory to store intermediate results, reducing the need for data transfers to and from external memory. Many AI chips are custom-designed to implement specific AI algorithms or perform particular AI tasks. Their unique hardware design makes AI chips up to 10,000 times faster and more power-efficient than traditional CPUs and GPUs.

AI chips vs. AI accelerator chips

AI chips and AI accelerator chips are often used interchangeably, but there are subtle differences. AI chips broadly refer to integrated circuits designed to accelerate AI workloads, encompassing everything from AI-adapted general-purpose processors to highly specialized ASICs.

On the other hand, AI accelerator chips, are a subset of AI chips specifically designed with specialized hardware units and architectures that significantly speed up AI computations. These chips optimize operations commonly found in AI algorithms, such as matrix multiplications and convolutions.

While all AI accelerator chips are AI chips, not all AI chips are accelerators. General-purpose processors like CPUs, GPUs, and certain FPGAs fall under the broader category of AI chips but lack the dedicated acceleration capabilities of true AI accelerators. Examples of AI accelerators include TPUs, ASICS, and neural processing units (NPUs).

Types of AI chips

Central processing units (CPUs): Although CPUs are not typically categorized as AI chips, some, like Intel Core and AMD Ryzen, can handle specific AI workloads. However, compared to specialized AI chips, these CPUs are limited in performance and energy efficiency.

Graphics processing units (GPUs): Originally designed for graphics processing, GPUs have been adapted for parallel computing essential to AI tasks. Their ability to efficiently handle matrix operations makes them widely used for training AI models. Popular GPUs for AI training include NVIDIA’s Tesla and AMD’s Instinct series.

GPUs typically feature multiple cores, offering more computing power than CPUs. But they rely on floating-point computing, making them less efficient for low-precision AI calculations.

Field-programmable gate arrays (FPGAs): FPGAs are reconfigurable hardware devices that can be programmed for a wide range of tasks, including AI workloads. They provide hardware-level flexibility, allowing for specialization in specific AI algorithms. FPGAs consume less power and have lower latency than CPUs or GPUs but are less versatile.

While GPUs are commonly used for training AI models, FPGAs are often used for inference tasks — applying trained AI models to real-world data. Examples include Xilinx Versal AI Engine, Intel Stratix 10, and Microchip SmartFusion2.

Application-specific integrated circuits (ASICs): ASICs are custom-designed for specific purposes, such as training or inference in AI models. They offer the highest performance and energy efficiency but are not reprogrammable like FPGAs. Designed with singular tasks in mind, ASICs often outperform other AI chips in the specific applications they were built for.

Tensor processing units (TPUs): Developed by Google, TPUs are ASICs designed for machine learning tasks involving tensor operations. They are known for their high performance and energy efficiency.

Neural processing units (NPUs): NPUs are AI chips tailored for deep learning and neural networks. Similar to GPUs, NPUs are optimized for processing large datasets, enabling tasks like object detection, speech recognition, and video editing. NPUs often outperform GPUs in certain AI processes and are commonly used in mobile and edge devices.

Neuromorphic chips: Inspired by the human brain, neuromorphic chips mimic the way neurons and synapses work. These chips use Spiking Neural Networks (SNNs), where spikes rather than continuous values represent information. Neuromorphic chips aim to achieve brain-like energy efficiency and learning capabilities.

Digital signal processors (DSPs): DSPs are designed for efficient signal processing operations like filtering and transformations, which align well with AI applications. They feature parallel processing architectures and are often included in AI systems-on-chip (SoCs) for tasks such as sensor fusion, audio/video processing, and edge AI. Their power efficiency makes them suitable for battery-powered devices.

What’s inside an AI chip?

Most modern AI chips are designed as system-on-a-chip (SoC) architectures to reduce size and power consumption. This design approach allows optimized data flow between components, minimizing delays and maximizing performance.

SoCs can be customized with various combinations of components, making them adaptable to a wide range of AI applications and performance requirements. The design is also scalable, meeting the needs of both resource-constrained edge devices and high-performance data centers.

Advanced AI chips are typically available as mobile SoCs, edge AI SoCs, or data center SoCs by simplifying design and reducing manufacturing costs. An AI SoC is a specialized integrated circuit designed to handle the computational demands of AI tasks efficiently.

It includes the following components:

AI accelerator: The core component responsible for handling AI computations. This may include a Neural Processing Unit (NPU), Tensor Processing Unit (TPU), or Digital Signal Processor (DSP).
Processor core: A general-purpose processor, often a CPU core, for system control, data management, and auxiliary tasks.
Memory subsystem: On-chip memory is often integrated for fast data access, while more significant off-chip memory (e.g., DRAM) may be attached to store larger datasets and intermediate results.
Connectivity: Interfaces for high-speed input/output and network communication enable data transfer to and from external devices and systems.
Power management unit (PMU): Optimizes the power consumption and performance of the SoC.

AI chip applications

Modern AI applications heavily rely on specialized AI chips. Key sectors driving their proliferation include:

Generative AI: Large language models (LLMs) and other generative AI applications depend on AI hardware, particularly high-performance GPUs and TPUs. These chips accelerate inference, enabling real-time generation of text, images, and other outputs.

Edge AI: Edge AI chips with specialized NPUs bring AI processing to devices at the edge of networks, such as IoT devices and smartphones. They enable real-time applications with low latency, like object detection, voice assistants, and predictive maintenance.

Robotics: AI chips help robots perceive their environment, make real-time decisions, and interact more effectively. They enhance navigation and obstacle avoidance and enable natural language understanding and human-like social interaction in robots.

Autonomous vehicles: AI chips process sensor data (e.g., cameras, LIDAR, radar) to help autonomous vehicles perceive surroundings, make driving decisions, and navigate safely. They control real-time vehicle operations like steering, acceleration, and braking.

Retail: AI chips power computer vision systems to analyze customer behavior, provide personalized recommendations, and optimize inventory. They also aid in real-time fraud detection and demand forecasting, reducing costs and enhancing operational efficiency.

Medicine: AI chips analyze vast datasets to identify drug candidates, predict efficacy, and optimize development pipelines. They also enable fast and accurate medical imaging analysis (e.g., X-rays, MRIs) for diagnosis and personalized treatment plans.

Mobiles and wearable technology: NPUs facilitate on-device AI processing in smartphones, wearables, and other mobile devices, improving performance, privacy, and responsiveness. These chips power voice assistants and other AI-driven features, enhancing user interactions.

Computer vision: AI chips accelerate object detection, image recognition, and video surveillance. They are instrumental in advancing computer vision systems for autonomous vehicles, medical imaging, and other applications.