AI chips are a new type of microprocessor designed to improve the performance of artificial intelligence-based applications.
Complex deep learning models cannot be supported by powerful general-purpose devices (such as CPUs). As a result, demand for AI chips with parallel computing capabilities has increased, and McKinsey expects this trend to continue.
Here are some interesting AI chips for generative AI.
LG’s smart gadgets benefit from LG Neural Engine, a hardware-based AI accelerator technology. Utilize hardware and software, including deep learning techniques, to complete complex machine learning tasks. Neural Engine can perform computations locally, without cloud access, and runs efficiently, helping preserve battery life. Built into LG’s OS, it communicates directly with the CPU to improve speed and efficiency. The LG Neural Engine typically drives faster, more accurate AI-driven functions to enhance the user experience.
Multi-core processors such as TI’s Cavium CN99xx Thunder X2 CPUs are ideal for data centers and clouds. Hardware acceleration for encryption, compression, and virtualization is built into up to 54 custom-designed cores, 3.0 GHz clock speed, and 1 terabyte of memory. The Thunder X2 central processing unit (CPU) is designed with high-performance computing in mind, is virtualization-ready, and works with a wide range of server operating systems and components. This is a robust and efficient processor for his HPC workloads in the cloud and data centers.
The Cerebras Wafer Scale Engine is a purpose-built processor designed to accelerate AI applications. With 1.2 trillion transistors and 400,000 AI-optimized processing cores, a single giant device can perform AI computations at unmatched scale and speed. The chip’s innovative layout makes it compatible with existing data center hardware. More processing cores, better memory and enhanced performance are just some of how the WSE-2 improves on his previous generation WSE. Both chips open new avenues for AI development and implementation.
Nvidia’s Jetson embedded computing boards are built to run artificial intelligence (AI) and computer vision software on devices at the network perimeter. Jetson products include entry-level supercomputers and development kits. These motherboards are designed to process artificial intelligence algorithms and incorporate Nvidia’s GPU technology, central processing units, and input/output ports. Jetson boards are used in autonomous robots, drones, medical devices, and industrial automation. Developers can leverage Nvidia’s SDK and his libraries like CUDA and cuDNN to create and deploy his AI apps on Jetson.
Amazon Web Services (AWS) created its own machine learning inference chip called AWS Inferentia to speed up deep learning applications in the cloud. Its primary purpose is the inference of complex machine learning models, which require computation of large neural networks. AWS Inferentia is designed with massive on-chip memory and computing cores, capable of running multiple computations simultaneously. As a result, production-ready machine learning models benefit from improved inference performance at a lower cost. Customers can use AWS services such as Amazon SageMaker and AWS Lambda with Inferentia to quickly build and run machine learning applications in the cloud. In addition to the Inferentia API, AWS provides software development kits (SDKs) and libraries such as his TensorFlow that programmers can use to create and tune machine learning models.
Qualcomm Hexagon Vector Extensions (HVX) are optimized for high-performance computing applications such as machine learning. HVX is a vector processing unit that executes instructions designed for machine learning tasks on multiple data items in parallel. It is compatible with well-known ML libraries such as TensorFlow and Caffe and includes many vector registers. Available as an embedded component of Snapdragon processors and as a standalone digital signal processor, HVX is a robust platform for bringing AI to more devices and applications.
The Colossus MK2 IPU processor is a new breed of massively parallel CPU built in conjunction with the Poplar SDK for AI acceleration. Revolutionary improvements in computing, networking, and memory in our silicon and system architecture deliver an 8x real-world performance improvement compared to the previous version of the Colossus IPU, his MK1 IPU.
