Blogs / AI Custom Chips: Revolution in Processing and Smart Computing

AI Custom Chips: Revolution in Processing and Smart Computing

چیپ‌های اختصاصی هوش مصنوعی: انقلاب در پردازش و محاسبات هوشمند

Introduction

In an era where artificial intelligence has become one of the most transformative technologies in the world, the need for powerful and efficient hardware to run complex machine learning algorithms is felt more than ever. Traditional processors like CPUs and even GPUs, which were initially designed for graphics processing, are no longer able to fully meet the heavy computational demands of modern AI models.
This challenge has driven the tech world toward designing and developing AI custom chips. These specialized chips, known by various names such as TPU, NPU, and ASIC, are specifically designed to accelerate AI processing operations and can increase computational efficiency several times over.
Tech giants like Google, Apple, Amazon, Microsoft, and even OpenAI are seriously investing in and developing their own custom chips for artificial intelligence. This intense competition has not only led to remarkable advances in hardware but is also shaping the future of smart computing.

What Are AI Custom Chips?

AI custom chips or AI Accelerators are specialized semiconductor chips specifically designed and optimized for performing machine learning and deep learning related computations. Unlike general-purpose processors that must handle a wide range of computational tasks, these chips focus on specific operations that are repeatedly used in AI algorithms.
The key feature of these chips is their ability to perform matrix operations and tensor computations at very high speeds with low energy consumption. In fact, instead of performing tasks serially like CPUs, they can process thousands of operations simultaneously and in parallel.

Main Types of AI Custom Chips

1. TPU (Tensor Processing Unit)

TPU or Tensor Processing Unit is a custom chip designed by Google, specifically optimized to accelerate machine learning computations and particularly deep neural network operations. These chips were first introduced in 2016, and since then, multiple generations have been developed.
Google recently introduced the seventh generation of TPU with the codename Ironwood, which will be offered in two configurations: 256-chip and 9,216-chip, with computational power reaching 4,614 teraflops per second. This new generation is expected to hit the market later this year.
TPUs are specifically designed to process Transformer models and large language models, and their key advantage is high efficiency - not only in performance but also in energy consumption.

2. NPU (Neural Processing Unit)

NPU or Neural Processing Unit is a specialized processor designed to accelerate neural network algorithms. These processors are embedded in Intel, AMD, and Apple silicon processors and are designed for consumer devices to be small, low-power, but fast enough.
NPUs are typically used for inference operations on local devices and support low-bit operations such as INT4, INT8, FP8, and FP16. The common metric for measuring NPU power is trillion operations per second (TOPS).
Apple's Neural Engine in the M4 chip family is up to twice as fast as the previous generation and can execute 38 trillion operations per second. This remarkable advancement has enabled running advanced AI models directly on personal devices.

3. ASIC (Application-Specific Integrated Circuit)

ASICs are application-specific integrated circuits designed for a particular application. ASIC accelerators for AI are chips specifically built for AI tasks like inference or training and provide high energy efficiency and performance for target workloads.
These types of chips are especially suitable for edge and embedded environments and are much more energy-efficient than general-purpose GPUs or FPGAs.

Tech Giants and Their Custom Chips

Google and TPU

Google has been a pioneer in designing AI custom chips. The company's TPUs are currently used in Google Cloud infrastructure for training and inference of deep learning models. Google has focused on producing powerful AI chips to meet the demand of large-scale projects.
The new generation TPU named Ironwood is expected to enable training and running massive language models like Gemini with unprecedented processing capacity, speed, and efficiency.

Apple and Neural Engine

Apple has revolutionized local AI computing with the introduction of its silicon chip family (M and A series). Apple's Neural Engine now processes over 60 trillion operations per second, and the Unified Memory Architecture enables efficient execution of machine learning models without data bottlenecks.
This advancement means that machine learning developers, creative professionals, and anyone working with AI models can perform heavy computational tasks directly on Mac or iPad.

Amazon and Trainium and Inferentia Chips

Amazon has also entered the competition with the development of two custom chip families: AWS Trainium for training models and AWS Inferentia for inference.
AWS Trainium chips are specifically designed by Amazon for AI training and inference to provide high performance with cost reduction and can reduce training costs by up to 50%.
Rufus, Amazon's AI-powered shopping assistant, reduced its costs by 4.5 times on Prime Day using over 80,000 Inferentia and Trainium chips while maintaining low latency for customers.
This success demonstrates how custom chips can provide significant efficiency at large scale.

Microsoft and Maia

Microsoft has also entered this field with the introduction of the Maia 100 custom chip. Maia 100 is Microsoft's first custom AI accelerator designed to optimize large AI workloads in Azure and provides high performance through hardware-software synergy.

OpenAI and Custom Chip Design

Even OpenAI is pursuing its own custom chip design. It's reported that OpenAI is finalizing the design of its chips and plans to start production through TSMC. This move shows that even leading software companies understand the importance of controlling custom hardware for developing GPT-5 and other advanced language models.

Advantages of AI Custom Chips

1. Higher Performance

Custom chips can perform AI operations several times faster than general-purpose processors. This high speed is especially critical in training large deep learning models and executing real-time inference.

2. Reduced Computational Costs

With increased efficiency, the need for computational resources decreases. This means lower cloud costs, less power consumption, and consequently significant savings in operational costs.

3. Optimized Energy Consumption

One of the main challenges in running AI models is high energy consumption. Custom chips can perform the same work with much lower energy consumption by optimizing their architecture for specific AI operations.

4. Better Scalability

The modular design of many custom chips enables easy scaling. For example, Google's TPUs can work in clusters of thousands of chips, providing enormous computational power for training giant Transformer models.

5. Improved User Experience

With faster execution of AI models, AI-based applications and services can provide faster responses to users. This is especially important in real-time applications such as natural language processing, facial recognition, and speech recognition.

Challenges and Limitations

1. High Development Cost

Designing and manufacturing a custom chip requires enormous investment. The process of design, manufacturing, testing, and mass production can cost billions of dollars.

2. Long Development Time

From initial idea to final production of a chip can take several years. This is while the AI world is changing at a very high speed and requirements may change during development time.

3. Limited Flexibility

Unlike GPUs that can be used for a wide range of applications, custom chips are optimized for specific tasks and may have low efficiency in other applications.

4. Software Ecosystem Dependency

Using custom chips requires specific tools, libraries, and software frameworks. Developers must familiarize themselves with these tools and rewrite their code to optimally utilize the hardware.

5. Supply Chain Issues

Manufacturing advanced chips requires a complex supply chain. Chip shortages and production limitations can affect availability and pricing.

Practical Applications of AI Custom Chips

1. Data Centers and Cloud Computing

The largest application of custom chips is in data centers. Cloud service providers like Google Cloud, AWS, and Azure use these chips to provide machine learning services to their customers.

2. Personal Smart Devices

Modern smartphones, laptops, and tablets are equipped with NPUs that enable running artificial intelligence capabilities like facial recognition, language translation, and voice assistants locally.

3. Autonomous Vehicles

The automotive industry is one of the largest consumers of AI chips. Autonomous vehicles need to process enormous amounts of sensor data quickly and in real-time, which is only possible with custom chips.

4. Medical Diagnosis

In the medical field, custom chips are used for rapid analysis of medical images, disease diagnosis, and drug discovery. AI application in diagnosis and treatment has significantly expanded with the help of these chips.

5. Robotics and Industrial Automation

Artificial intelligence and robotics heavily require efficient chips. Industrial and service robots must be able to make decisions in real-time, which is only possible with fast data processing.

6. Cybersecurity

In cybersecurity, custom chips are used for rapid threat detection, analyzing suspicious patterns, and responding to attacks.

7. Creative Content Production

Generative AI for producing images, videos, and text requires high computational power. Tools like Midjourney, DALL-E, and Sora all use custom chips for content production.

The Future of AI Custom Chips

Quantum Computing and AI

One of the most exciting future horizons is the combination of quantum computing with artificial intelligence. Quantum artificial intelligence can bring computational capabilities to a level that is currently unimaginable.

Edge AI and Local Processing

The trend toward Edge AI and local processing is strengthening. Smaller, more efficient, and more powerful chips enable running complex models directly on IoT devices, security cameras, and sensors.

New Architectures

Researchers are working on novel architectures such as Neuromorphic Computing inspired by the structure of the human brain. These architectures can bring energy efficiency to unprecedented levels.

Integration with Other Technologies

Combining AI chips with technologies like blockchain and Internet of Things can enable new applications.

Smaller and More Efficient Language Models

The trend of developing Small Language Models (SLM) shows that the future doesn't necessarily mean bigger models. Custom chips can run smaller but more efficient models at high speed.

Standardization and Industry Collaboration

With the growth of the custom chip market, the need for common standards and protocols is increasingly felt. This standardization can accelerate ecosystem development.

Economic and Social Impact

Creating New Job Opportunities

Custom chip development has led to the creation of new jobs in hardware design, system programming, and algorithm optimization.

Democratization of Artificial Intelligence

With reduced computational costs, access to the processing power needed to run advanced models becomes available to smaller companies and startups.

Environmental Impact

Reducing energy consumption in data centers can have a significant impact on reducing the carbon footprint of the tech industry. Given that modern data centers are large energy consumers, optimizing consumption through custom chips can be an important step toward environmental sustainability.

Geopolitical Competition

Control of advanced chip manufacturing technology has become one of the key geopolitical issues. Countries are investing heavily in their semiconductor industries to reduce dependence on the global supply chain.

Comparing Custom Chips with GPUs

One common question is whether custom chips will replace GPUs? The answer is that these two technologies are more complementary than rivals.

GPU Advantages

  • High flexibility: GPUs can be used for a wide range of applications
  • Rich software ecosystem: Tools like CUDA and PyTorch and TensorFlow have excellent GPU support
  • Easy access: GPUs are readily available in the market

Custom Chip Advantages

  • Higher efficiency for specific tasks: For specific work, custom chips can be several times faster
  • Lower energy consumption: Better energy efficiency at large scale leads to significant savings
  • Higher density: More custom chips can be placed in limited space
In practice, many large systems use a combination of both. For example, GPUs may be used for research, development, and testing new models, while custom chips are employed for deployment and running final models at large scale.

The Role of Custom Chips in Large Language Models

One of the most important applications of custom chips is training and running large language models like GPT, Claude, and Gemini.
These models typically have billions of parameters and training them requires enormous computational power. Without custom chips, the cost and time of training these models would be unbearable.
For example, training a language model with 175 billion parameters (similar to GPT-3) on regular GPUs could take weeks or even months, while using TPU or similar custom chips reduces this time to days.
Also for inference, which must be done in milliseconds to provide an acceptable user experience, custom chips play a critical role. ChatGPT and other AI chatbots can only respond to millions of users simultaneously with the help of these advanced chips.

Optimization Techniques for Custom Chips

Quantization

One of the most important techniques for optimal utilization of custom chips is quantization. This method reduces model weights from 32-bit precision to 8-bit or even 4-bit, resulting in significant reduction in model size and increased speed.

Pruning

Pruning is the process of removing unnecessary connections in a neural network. This technique can make the model smaller and faster without significantly reducing accuracy.

LoRA and Efficient Fine-tuning

Techniques like LoRA (Low-Rank Adaptation) enable fine-tuning large models with fewer computational resources, making the use of custom chips even more efficient.

Mixture of Experts (MoE)

The Mixture of Experts architecture allows only the parts of the model that are necessary for a specific input to be activated, which significantly increases computational efficiency.

Strategies for Choosing the Right Chip

Choosing the right chip depends on various factors:

1. Type of Workload

Is your need model training or inference? Training requires more powerful chips, while for inference, more efficient and low-power chips can be used.

2. Project Scale

For small and research projects, general-purpose GPUs may be sufficient. But for large-scale deployment, custom chips have greater economic efficiency.

3. Budget Constraints

The initial cost of investing in custom chips is high, but in the long term it can create significant savings.

4. Software Ecosystem

Check whether the tools and libraries you use support the intended chip or not.

5. Energy and Space Requirements

In edge computing and mobile devices, energy consumption and physical size of the chip are extremely important.

Industry Outlook and Future Predictions

Market Growth

The AI chip market is experiencing explosive growth. It's predicted that this market will reach a value of hundreds of billions of dollars by the end of this decade.

Entry of New Players

In addition to tech giants, startup companies are also entering this field. This competition will lead to more innovations and price reductions.

Focus on Energy Efficiency

With increased awareness of environmental issues, designing low-power but powerful chips is becoming a higher priority.

Greater Integration

The future belongs to systems that integrate different processors (CPU, GPU, NPU, and other accelerators) seamlessly in one chip.

Conclusion

AI custom chips are not just a technical advancement, but a necessity for the future of smart computing. These chips, by providing higher performance, better energy efficiency, and cost reduction, enable the development and deployment of more complex AI models.
From Google's TPU to Apple's Neural Engine, from Amazon's Trainium to Microsoft's Maia, each of these chips plays an important role in shaping the future of artificial intelligence. Competition in this field not only benefits tech companies but also brings advantages for the entire human society.
With continuous advancement in new architectures such as neuromorphic computing, federated learning, and Edge AI, the future of AI custom chips looks brighter than ever.
Ultimately, success in the world of artificial intelligence depends not only on smart algorithms but also on powerful and efficient hardware that can execute these algorithms with the necessary speed and accuracy. AI custom chips precisely fulfill this need and are the bridge between theoretical ideas and practical applications of artificial intelligence.