AI chips are a new type of microprocessor designed to improve the performance of artificial intelligence-based applications.

The complex deep learning models cannot be supported by powerful general-purpose devices (such as CPUs). As a result, AI chips with parallel computing capabilities are in high demand, and McKinsey predicts that this trend will continue.

The following are the interesting AI chips for generative AI:

LG Neural Engine

LG's smart gadgets benefit from the LG Neural Engine, a hardware-based AI accelerator technology. It utilizes hardware and software, including deep learning techniques, to complete complex machine-learning tasks. The Neural Engine can perform computations locally, without access to the cloud, and it does it efficiently that helps preserve battery life. It's built into LG's OS and communicates directly with the CPU to improve speed and efficiency. The LG Neural Engine generally facilitates faster, more accurate AI-driven functionality and enhances the user experience.

TI Cavium CN99xx Thunder X2 CPU

Multi-core processors like TI's Cavium CN99xx Thunder X2 CPU are ideal for data centres and the cloud. Hardware acceleration for cryptography, compression, and virtualization is built into its up to 54 custom-designed cores, 3.0 GHz clock speed, and one 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. It's a robust and efficient processor for HPC workloads in the cloud and data centres.

Cerebras Systems WSE

Cerebras Wafer Scale Engine is a dedicated processor designed to speed up AI applications. A single massive device with 1.2 trillion transistors and 400,000 AI-optimized processing cores may do AI computations at an unparalleled scale and speed. The chip's innovative layout makes it compatible with preexisting data centre hardware. More processing cores, better memory, and enhanced performance are just a few of how the WSE-2 improves upon its predecessor, the WSE. Both chips open up new avenues for the development and implementation of AI.

Jetson – Nvidia

Nvidia's Jetson-embedded computing boards are built to run artificial intelligence (AI) and computer vision software on devices in the network's periphery. Jetson products include supercomputers and development kits for beginners. These motherboards are designed to process artificial intelligence algorithms and incorporate Nvidia's GPU technology, a central processing unit, and input/output ports. Jetson boards are used in autonomous robots, drones, medical devices, and industrial automation. Developers can create and deploy AI apps on Jetson with the help of Nvidia's SDK and libraries like CUDA and cuDNN.

Amazon AWS Inferentia

Amazon Web Services (AWS) created a unique machine learning inference chip called AWS Inferentia to improve the speed of deep learning applications in the cloud. Its intended purpose is inferencing complex machine learning models, necessitating massive neural network computation. AWS Inferentia is designed with vast on-chip memory and computing cores, allowing it to execute several computations simultaneously. As a result, production-ready machine learning models benefit from improved inference performance at a lower cost. Customers can quickly build and run machine learning applications in the cloud using AWS services like Amazon SageMaker and AWS Lambda with Inferentia. In addition to the Inferentia API, AWS offers a software development kit (SDK) and libraries like TensorFlow that programmers may use to create and tune their machine-learning models.

Qualcomm Hexagon Vector Extensions

Qualcomm Hexagon Vector Extensions (HVX) is optimized for high-performance computing applications like machine learning. HVX is a vector processing unit that executes instructions designed for machine learning tasks in parallel with multiple data items. It's compatible with well-known ML libraries like TensorFlow and Caffe and includes many vector registers. HVX is a robust platform for introducing AI to more devices and applications thanks to its availability as an embedded component of Snapdragon processors and a standalone digital signal processor.

Graphcore - Colossus MK2 GC200 IPU

Colossus MK2 IPU processor is a new kind of massively parallel CPU, built in tandem with the Poplar SDK to speed up AI. With revolutionary improvements in computing, networking, and memory in our silicon and systems architecture, we increased real-world performance by a factor of eight compared to the MK1 IPU, the previous iteration of our Colossus IPU.

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