AI Chips

Extracted Attributes

• Types

  Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Tensor Processing Units (TPUs), Neural Processing Units (NPUs)

• Purpose

  Process large volumes of data for AI workloads, provide faster processing capabilities, enable sophisticated robotics applications, facilitate extensive AI model training in data centers.

• Definition

  Specialized integrated circuits designed to handle AI tasks, accelerating execution of AI algorithms, typically involving large-scale matrix operations and parallel processing.

• Importance

  Essential for meeting demand for higher processing power, speed, and efficiency in AI, foundational for AI workloads across the pipeline, critical for reducing AI cost and latency.

• Applications

  Robotics (assistive technology, healthcare robots, industrial automation, warehouse logistics), data centers (computer vision, natural language processing, content generation), advanced control systems.

• Key Characteristics

  Smaller in size, manifold more efficient than standard chips, provide compute power with faster processing capabilities and smaller energy footprints, AI-optimized design features for identical, predictable, independent calculations.

Timeline

• DeepSeek, a Chinese AI company that develops large language models, was founded by Liang Wenfeng. (Source: Wikipedia)

  2023-07

• Sam Altman experienced a period of firing and rehiring at OpenAI, during which discussions about the future of AI and the importance of AI chips were prominent. (Source: Document 8905c897-bf22-4c6e-a62d-73123999ebf4)

  2023-11

• Trade restrictions on AI chip exports to China are in effect, leading companies like DeepSeek to innovate with less powerful chips. (Source: Summary, Wikipedia)

  Ongoing

• OpenAI is moving towards a model of continuous model improvement for systems like GPT-4, which necessitates a robust infrastructure of AI chips. (Source: Document 8905c897-bf22-4c6e-a62d-73123999ebf4)

  Ongoing

• DeepSeek's breakthrough with cost-effective, high-performing AI models caused Nvidia's share price to drop sharply, losing US$600 billion in market value. (Source: Wikipedia)

  Recent

• DeepSeek launched an eponymous chatbot alongside its DeepSeek-R1 model. (Source: Wikipedia)

  2025-01

DeepSeek

Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., doing business as DeepSeek, is a Chinese artificial intelligence (AI) company that develops large language models (LLMs). Based in Hangzhou, Zhejiang, Deepseek is owned and funded by the Chinese hedge fund High-Flyer. DeepSeek was founded in July 2023 by Liang Wenfeng, the co-founder of High-Flyer, who also serves as the CEO for both of the companies. The company launched an eponymous chatbot alongside its DeepSeek-R1 model in January 2025. Released under the MIT License, DeepSeek-R1 provides responses comparable to other contemporary large language models, such as OpenAI's GPT-4 and o1. Its training cost was reported to be significantly lower than other LLMs. The company claims that it trained its V3 model for $6 US million—far less than the $100+ US million cost for OpenAI's GPT-4 in 2023—and using approximately one-tenth the computing power consumed by Meta's comparable model, Llama 3.1. DeepSeek's success against larger and more established rivals has been described as "upending AI". DeepSeek's models are described as "open weight," meaning the exact parameters are openly shared, although certain usage conditions differ from typical open-source software. The company reportedly recruits AI researchers from top Chinese universities and also hires from outside traditional computer science fields to broaden its models' knowledge and capabilities. DeepSeek significantly reduced training expenses for their R1 model by incorporating techniques such as mixture of experts (MoE) layers. The company also trained its models during ongoing trade restrictions on AI chip exports to China, using weaker AI chips intended for export and employing fewer units overall. Observers say this breakthrough sent "shock waves" through the industry which were described as triggering a "Sputnik moment" for the US in the field of artificial intelligence, particularly due to its open-source, cost-effective, and high-performing AI models. This threatened established AI hardware leaders such as Nvidia; Nvidia's share price dropped sharply, losing US$600 billion in market value, the largest single-company decline in U.S. stock market history.

Web Search Results

• AI Chips: What Are They? - Built In

  An AI chip is a specialized integrated circuit designed to handle AI tasks. Graphics processing units (GPUs), field programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) are all considered AI chips. [...] Artificial Intelligence Editors' Picks # AI Chips: What Are They? An AI chip is an integrated circuit designed specifically for use in AI systems and tasks. The future of artificial intelligence largely hinges on the development of AI chips. Written by Ellen Glover Image: Shutterstock UPDATED BY Brennan Whitfield | May 01, 2025 [...] AI chips refer to specialized computing hardware used in the development and deployment of artificial intelligence systems. As AI has become more sophisticated, the need for higher processing power, speed and efficiency in computers has also grown — and AI chips are essential for meeting this demand. ## What Is an AI Chip?

• AI Chips Explained: How AI Chips Work, Industry Trends, Applications

  Robotics: AI chips are the brains underlying sophisticated robotics applications, ranging from assistive technology and healthcare robots to industrial automation and warehouse logistics. They provide robots the ability to sense their environment, gain experience, and accurately and deftly carry out difficult jobs. [...] # AI Chips Explained: How AI Chips Work, Industry Trends, Applications AI chips are specialized processors designed to accelerate the execution of artificial intelligence tasks, typically involving large-scale matrix operations and parallel processing. Aug 29, 2024 · 7 min read Complex AI algorithms require massively parallel computations and specialized operations, which put increasing pressure on traditional processors such as CPUs and GPUs. [...] Data centers: AI chips facilitate extensive AI model training in data centers, leading to advancements in computer vision, natural language processing, and other related domains. They are the driving forces behind the creation of highly developed AI models that can recognize objects and scenes with astounding precision, interpret and produce language akin to that of a person, and even produce unique material.

• AI Chips: What They Are and Why They Matter - CSET

  AI chips include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs) that are specialized for AI. General-purpose chips like central processing units (CPUs) can also be used for some simpler AI tasks, but CPUs are becoming less and less useful as AI advances. (Section V(A).) [...] 1. Our definition of “AI chips” includes graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and certain types of application-specific integrated circuits (ASICs) specialized for AI calculations. Our definition also includes a GPU, FPGA, or AI-specific ASIC implemented as a core on system-on-a-chip (SoC). AI algorithms can run on other types of chips, including general-purpose chips like central processing units (CPUs), but we focus on GPUs, FPGAs, and AI-specific ASICs [...] Like general-purpose CPUs, AI chips gain speed and efficiency (that is, they are able to complete more computations per unit of energy consumed) by incorporating huge numbers of smaller and smaller transistors, which run faster and consume less energy than larger transistors. But unlike CPUs, AI chips also have other, AI-optimized design features. These features dramatically accelerate the identical, predictable, independent calculations required by AI algorithms. They include executing a large

• AI Chips: Mechanism, Applications, and Trends Explained

  THE ANATOMY OF AI CHIPS- ARCHITECTURE AND DESIGN AI chips, aka logic chips, have the power to process large volumes of data needed for AI workloads. They are typically smaller in size and manifold more efficient than those in standard chips, providing compute power with faster processing capabilities and smaller energy footprints. The transition from Transistors to Tensors [...] Venturing into the world of robotics and advanced control systems, AI chips are increasingly playing an increasingly critical role. AI chips for Robotics are designed to process sensor data and make split-second decisions. This is essential for applications ranging from industrial automation to humanoid robots already in deployment. THE HUMAN ELEMENT: CAREERS IN AI ENGINEERING [...] The Alchemy of AI: Algorithms and Frameworks We would be amiss if we did not explain the fundamental functions of AI chips. So here it goes: AI chips serve a purpose, and the primary purpose of AI chips is in the use of neural networks, those complex mathematical models inspired by biological neural networks that constitute the human brain. Neural networks are composed of layers of interconnected nodes, that form the foundation of deep learning.

• Artificial Intelligence (AI) Processors and AI Chips

  By AI processors and AI chips are the foundation for AI workloads across the pipeline. These hardware components handle the computational demands of AI applications, which can vary greatly based on use case and complexity. Matching the right processor type to your AI workload and performance expectations is critical to enabling practical, scalable AI results. [...] AI chips also play a critical role in addressing constantly evolving AI processing needs. This market category—a relatively recent and still-maturing evolution—includes both general-purpose devices like a graphics processing unit (GPU) or field-programmable gate array (FPGA) being applied to AI workloads and purpose-built AI technologies, including tensor processing units (TPUs) and neural processing units (NPUs). In many cases, AI processors can include other AI chips, such as GPUs and NPUs, [...] AI processors and AI chips encompass CPUs and discrete acceleration hardware, including GPUs, FPGAs, and purpose-built AI accelerators such as neural processing units (NPUs). Some AI processing needs can be handled by a stand-alone CPU, especially those with integrated accelerations and optimizations. Complex AI needs require additional hardware beyond the CPU to unlock more performance through a parallel computing approach.