Beyond Silicon: The Rise of AI Chip Companies
Are Artificial Intelligence Chips the Future of Tech? Discover How Silicon's Successor is Transforming the Industry!
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Artificial Intelligence (AI) has come a long way since the first time it was introduced to the world. Today, it is impossible to imagine several industries and sectors without the aid of AI-powered machines and systems. The growth of AI is testament to the development of its building blocks, one of which is chips. AI chips are the hardware accelerators designed to work alongside traditional CPUs (Central Processing Units) and GPUs (Graphics Processing Units) to enable quicker and more efficient computations. This curated blog provides an overview of the top AI chip companies that are revolutionizing the AI game.
NVIDIA: Leading the AI Chip Market
When talking about AI-enabled GPUs, NVIDIA Corporation is a household name. NVIDIA is a leading AI chip manufacturing company that has been instrumental in the evolution and development of GPUs that support machine learning. Capitalizing on its years of experience in the graphics field, the company has developed some of the most powerful and efficient GPU based AI chips in the market.
The Tesla V100 is NVIDIA’s flagship AI chip that features more than 5,000 processing cores, delivering a whopping 125 trillion floating-point operations per second (TFLOPS) and 16 GB of high-bandwidth memory. It also boasts of Tensor Cores that can work 12 times as fast as non-Tensor Core processors, making it an ideal choice for complex AI projects. With its incredible processing power, the Tesla V100 is the go-to choice for data scientists and machine learning experts seeking high-speed processing capabilities in their systems.
Intel: A Reliable and Trustworthy AI Chip Provider
Intel is a well-known name in the computing and electronics industry with decades of experience in producing high-quality and reliable processors. In the AI chip industry, Intel is known for developing its line of Neural Network Processors (NNP), which makes use of an interconnected mesh configuration that accelerates computing power.
The Intel Nervana Neural Network Processor (NNP) is designed to deliver parallel processing, as it provides multi-billion connections through four core blocks, enabling data movement and computation in parallel. In addition to that, its software framework, Neon, allows adjusting of tensor shapes, making it possible to accept data from almost any format. This flexibility enables Intel to deliver software-based solutions for deep learning capabilities.
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Google: Providing Unique AI Chip Solutions
The world's most-used search engine, Google, stands out for its innovative applications of AI chips. Google is known for its development of the Tensor Processing Unit (TPU), a specialized ASIC designed to deliver highly optimized data processing for machine learning applications.
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The Google TPU is built to run TensorFlow, Google's machine learning software library, and is used in the tech giant's data centers to accelerate the performance of their cloud-based machine learning solutions. Google's TPUs are believed to provide 15-30x speed increases compared to GPUs and CPUs, making them an ideal solution for handling the massive amounts of data used for machine learning.
Qualcomm: Designing AI Chip for the Future
Qualcomm is well-known for being the leader in the mobile processor industry. Still, the company is branching out to develop chips for wearable and other embedded devices, including AI chips. Its Snapdragon Neural Processing Engine (NPE) is designed to deliver real-time processing capabilities for low-power, highly compact micro-devices.
The Snapdragon NPE comprises an extensive library of instructions and programming features, providing design flexibility for smart home devices, robotics, and other IoT applications requiring AI. It uses deep learning frameworks such as TensorFlow, Caffe, and Theano, among others, to provide high-performance processing for lower system power consumption.
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