AI Chips
- [Stanford University]
- Overview
Artificial intelligence (AI) chips are computer microchips that are designed to perform the complex calculations required by AI systems. They are used for tasks such as training and deploying AI algorithms, machine learning (ML), and data analysis.
AI chips are different from traditional computer chips because they have advanced design features that allow them to perform many calculations in parallel, use low-precision calculations, and optimize memory access.
AI chips are used in many different applications, including:
- Large language models: AI chips speed up the training and refinement of AI algorithms, which are essential for systems like ChatGPT
- Edge AI: AI chips make it possible to process AI on smart devices like cameras, watches, and kitchen appliances
- Autonomous vehicles: AI chips help driverless cars process and interpret data from sensors, cameras, and LiDAR
AI chips are typically designed as a "system-on-chip" (SoC), which is a computer chip that contains multiple functions beyond the central processing unit (CPU). An SoC might include parts that process video and images, store memory, and perform ML tasks.
Some types of AI chips include: Graphics processing units (GPUs), Field-programmable gate arrays (FPGAs), and Application-specific integrated circuits (ASICs).
- Why Are AI Chips Important?
AI chips, or Neural Processing Units (NPUs), are important because they are designed to perform AI tasks with greater accuracy and efficiency than regular chips. AI chips can be used in many fields, including healthcare, finance, transportation, and entertainment.
With an AI chip, AI algorithms can process data at the edge of a network, with or without an internet connection, in milliseconds. Edge AI enables data to be processed where it is generated rather than in the cloud, reducing latency and making applications more energy efficient.
Here are some reasons why AI chips are important:
- Accuracy: AI chips can perform complex calculations with precision, which reduces the likelihood of errors. This makes them a good choice for high-stakes applications like medical imaging and autonomous vehicles.
- Efficiency: AI chips can be tens or thousands of times faster and more efficient than CPUs for training and inferring AI algorithms. This can lead to faster and more affordable AI development.
- Parallel processing: AI chips can execute tasks in parallel, which can help speed up operations in neural networks. This can enhance the performance of AI applications like chatbots and generative AI.
- Robotics: AI chips' machine learning (ML) and computer vision capabilities can help robots detect and react to changes in their environment with speed and subtlety. This can be useful for AI-enhanced robots like security guards and personal companions.
[More to come ...]
