GPU and AI Semiconductors: Understanding Chips (CHIP)


Why Is Computing Power Important?

ChatGPT has been making headlines recently. Where is the ball thrown by OpenAI headed? With the release of GPT-4, interest in large-scale AI models and generative AI has surged. GPT-3 alone boasts a staggering 175 billion parameters, requiring immense computational power to process vast amounts of data efficiently. As a result, the ability to compute at high speeds has become more crucial than ever.

Computational power is equally essential in the field of mobility. Autonomous driving, for instance, relies on rapid data processing. Vehicles equipped with autonomous driving technology must interpret real-time inputs from cameras, LiDAR, radar, and various other sensors. To ensure driving safety, these vehicles require near-instantaneous computing capabilities.

Tesla has been making strides in this area by developing its own AI semiconductor, the Dojo Chip, designed to enhance its Full Self-Driving (FSD) system through accelerated AI processing. Similarly, NVIDIA, the leader in GPU technology, has expanded the role of graphics processors into GPGPU (General-Purpose computing on Graphics Processing Units), laying the foundation for a broader AI ecosystem applicable across various industries, including mobility.

What Is a GPU?

The Graphics Processing Unit (GPU), a technology spearheaded by NVIDIA, was originally designed to power computer graphics, particularly for video games. It rapidly calculates pixel colors and resolutions to deliver smooth, high-quality visuals.

Rendering detailed graphics in real-time requires vast computational resources, and traditional Central Processing Units (CPUs) were not well-suited for the task. Unlike a CPU, which relies on a limited number of cores to execute complex tasks sequentially, a GPU is optimized for parallel processing, enabling it to perform thousands of simple calculations simultaneously.

Over time, GPUs have found new applications beyond gaming. With advancements in digital technology, industries such as film and animation—led by studios like Disney—have embraced 3D modeling and CGI (Computer-Generated Imagery) to create lifelike digital visuals. NVIDIA has further pushed the boundaries with its Omniverse platform, a specialized service designed for 3D visualization, accelerating the expansion of the metaverse, augmented reality (AR), and virtual reality (VR).

Moreover, GPUs have evolved beyond their original role in rendering graphics. The development of GPGPU technology has transformed them into versatile computational tools capable of handling tasks traditionally managed by CPUs. NVIDIA’s CUDA (Compute Unified Device Architecture) has played a pivotal role in this transformation, solidifying its dominance in the high-performance computing space.

Why AI Semiconductors?

If GPUs already offer high computational power, why do we need AI semiconductors? What advantages do they bring?

AI semiconductors are designed for highly specialized computing tasks, offering greater efficiency than GPUs in specific AI applications. One of their biggest strengths is their superior energy efficiency, reducing power consumption while maintaining high performance.

For instance, in the financial sector, AI semiconductors can be tailored for algorithmic trading, rapidly processing stock market data to enable real-time automated trading (robo-advisors). Unlike GPUs, which are built for general-purpose computing, AI chips are optimized for dedicated tasks, making them faster and more efficient in specialized domains.

However, while GPUs benefit from widely accessible architectures and programming frameworks, AI semiconductors often require customized programming approaches, making them less accessible to developers.

Despite this challenge, AI semiconductors are projected to account for one-third of the global semiconductor market in the coming years. As semiconductor technology continues to advance, businesses are increasingly prioritizing enhanced user experiences and real-time AI performance, further driving demand for AI-specific computing solutions. AI semiconductors are poised to shape the future of computing, offering industries the ability to focus computing power on their precise technological needs like never before.

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About the Author: Drytree

About the Author: Drytree (Jaehoon Lee) I am a senior researcher specializing in deep tech startups, technology commercialization, and R&BD strategy at a national research institute in South Korea. With a deep curiosity about the rapid evolution of technology and its impact on society, I have been writing under the pen name Drytree since 2021, aiming to make complex technological trends more accessible and foster informed discussions on innovation and policy. My academic background is diverse, spanning Political Science (B.A.), International Politics (M.A.), and currently, a Ph.D. in Technology Management & Entrepreneurship . I have published 13 academic papers in domestic and international journals, covering topics such as patent analysis, technology security, and industrial policy . Books & Publications I have authored several books exploring technology, startups, and geopolitical implications of innovation , including: Elon Musk’s DOGE: The Dawn of a New Economic Exper...
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