Seven Intriguing Generative AI Chips

AI chips are a new type of microprocessor developed to boost the performance of AI-based applications.

Powerful general-purpose hardware (such as CPUs) cannot support complicated deep learning models. As a result, AI chips with parallel computing capabilities are in high demand, according to McKinsey, and this trend is expected to continue.

The following AI chips are promising for generative AI:

Neural Engine from LG
The LG Neural Engine, a hardware-based AI accelerator engine, powers LG’s smart devices. It completes complicated machine-learning tasks by utilising hardware and software, including deep learning techniques. The neural engine can do computations locally without requiring access to the cloud, and it does it effectively, preserving battery life. It is integrated into LG’s operating system and interfaces directly with the CPU to boost speed and efficiency. In general, the LG Neural Engine enables faster, more accurate AI-driven functionality and improves the user experience.

CPU: TI Cavium CN99xx Thunder X2
Multi-core processors, such as TI’s Cavium CN99xx Thunder X2 CPU, are perfect for data centres and cloud computing. Its up to 54 custom-designed cores, 3.0 GHz clock speed, and one terabyte of memory include hardware acceleration for cryptography, compression, and virtualization. The Thunder X2 Central Processing Unit (CPU) is intended for high-performance computing, is virtualization-ready, and is compatible with a wide range of server operating systems and components. It’s a tough and efficient processor for cloud and data centre HPC applications.

WSE Cerebras Systems
Cerebras Wafer Scale Engine is a dedicated processor developed to improve the performance of AI applications. A single enormous device with 1.2 trillion transistors and 400,000 AI-optimised processing cores has the potential to do AI computations at unprecedented scale and speed. The chip’s novel design makes it compatible with existing data centre technology. More processing cores, improved memory, and improved performance are just a few of the ways the WSE-2 outperforms its predecessor, the WSE. Both chips open up new possibilities for AI development and deployment.

Nvidia: Jetson
Nvidia’s Jetson-embedded computing boards are designed to run artificial intelligence (AI) and computer vision software on network edge devices. Supercomputers and development kits for beginners are among the Jetson goods. These motherboards are intended to perform AI algorithms and include Nvidia’s GPU technology, a central processor unit, and input and output connectors. Autonomous robotics, drones, medical gadgets, and industrial automation all employ Jetson boards. Developers can use Nvidia’s SDK and libraries, such as CUDA and cuDNN, to design and deploy AI apps on Jetson.

Amazon Web Services Inferentia
AWS Inferentia is a unique machine learning inference chip developed by Amazon Web Services (AWS) to improve the pace of deep learning applications on the cloud. Its intended use is to infer complicated machine learning models, which requires large neural network processing. AWS Inferentia is built with a large amount of on-chip memory and computational processors, allowing it to perform multiple computations at the same time. As a result, production-ready machine learning models have better inference performance at a lower cost. Customers can use Inferentia to quickly design and run machine learning applications in the cloud using AWS technologies such as Amazon SageMaker and AWS Lambda. Aside from the Inferentia API, AWS provides a software development kit (SDK) and libraries such as TensorFlow that programmers can use to build and refine machine-learning models.

Hexagon Vector Extensions from Qualcomm
The Qualcomm Hexagon Vector Extensions (HVX) architecture is designed for high-performance computing applications such as machine learning. HVX is a vector processing unit that runs machine learning instructions in parallel with numerous data items. It is interoperable with well-known ML libraries such as TensorFlow and Caffe, and it provides a large number of vector registers. Because it is available as an embedded component of Snapdragon processors as well as a standalone digital signal processor, HVX is a robust foundation for introducing AI to additional devices and applications.

IPU Graphcore: Colossus MK2 GC200
The Colossus MK2 IPU processor is a new type of massively parallel CPU designed in collaboration with the Poplar SDK to accelerate AI. We enhanced real-world performance by a factor of eight compared to the MK1 IPU, the previous generation of our Colossus IPU, thanks to breakthrough enhancements in computing, networking, and memory in our silicon and systems architecture.