Top 8 AI Chips for Your Generative AI Play
In 2023, the introduction of ChatGPT and DALL-E 2, large language models, brought generative AI to the forefront of public attention, resulting in unprecedented levels of excitement around generative AI. This has made chips that can handle AI at a large scale more important than ever. The AI chip market is expected to grow at a CAGR of 51% and reach $73.49 billion by 2025. Semiconductor companies could capture 40-50% of the total market share. Companies such as Alphabet, Broadcom, Intel, NVIDIA, Qualcomm, Samsung Electronics, and TSMC make chips used to train AI models. According to research, NVIDIA has captured 88% of the GPU market. Consequently, a lot of users consider NVIDIA as the primary beneficiary of the flourishing generative AI domain. But there are other significant players like Cerebras, Alphabet, and IBM too who have forayed into this domain. Here’s a list of some of the top AI chips: Jetson – Nvidia Jetson is a line of embedded computing boards from Nvidia that are designed to power AI and computer vision applications in edge devices. The Jetson platform includes a range of products, from entry-level development kits to high-performance supercomputers. These boards feature Nvidia’s GPU technology, as well as CPU and I/O capabilities, and are optimised for running deep learning models and other AI algorithms. Jetson boards are commonly used in applications such as autonomous robots, drones, medical devices, and industrial automation. Nvidia also provides a software development kit (SDK) and libraries, including CUDA and cuDNN, that enable developers to build and deploy AI applications on Jetson. Cerebras Systems WSE The Cerebras Wafer Scale Engine is a specialised chip that accelerates AI workloads. It is a large single chip with 1.2 trillion transistors and 400,000 AI-optimised processing cores that work together to perform AI computations at an unprecedented scale and speed. The chip’s unique design allows it to be easily integrated into existing data centre infrastructure. The WSE has a successor called the WSE-2, which has significant improvements over the original WSE, including more processing cores, improved memory, and performance. Both chips offer new possibilities for AI research and deployment. Amazon AWS Inferentia AWS Inferentia is a custom-designed machine learning inference chip developed by Amazon Web Services (AWS) to accelerate the performance of deep learning applications in the cloud. It is specifically designed to optimise the processing of large-scale neural networks used for machine learning inference. AWS Inferentia is built with a high number of on-chip memory and processing cores, enabling it to perform a large number of computations in parallel. This results in faster and more cost-effective inference performance for machine learning models in production. Inferentia is integrated into AWS services, such as Amazon SageMaker and AWS Lambda, allowing users to easily deploy and run machine learning applications in the cloud. AWS also provides a software development kit (SDK) and libraries, such as TensorFlow, to enable developers to build and optimise their machine learning models for Inferentia. IBM Power10 In August of 2021, IBM announced the Power10, a microprocessor. It has been designed to offer high performance and scalability for enterprise workloads in AI, cloud computing, and hybrid cloud environments. Power10 has 18 billion transistors and is made using a 7nm process technology. It comes with up to 15 processor cores that can execute up to 8 threads simultaneously, enabling it to handle 120 threads concurrently. The chip’s advanced memory features include support for HBM2e memory, which delivers four times the memory bandwidth of DDR4. Additionally, it has new hardware-based security features like transparent memory encryption and secure boot, which provide protection against cyber threats. Power10 is a robust and flexible microprocessor that can meet the requirements of contemporary enterprise workloads, particularly in AI and cloud computing. Later, during mid 2022, IBM announced the expansion of its Power10 server line, introducing mid-range and scale-out systems to enhance and automate business applications and IT operations. Qualcomm Hexagon Vector Extensions Qualcomm Hexagon Vector Extensions (HVX) is a hardware platform developed by Qualcomm for mobile and embedded devices. It is designed to accelerate machine learning and other high-performance computing workloads. HVX is a vector processing unit that processes multiple data elements in parallel with optimised instructions for machine learning workloads. It has a large number of vector registers and supports popular machine learning frameworks like TensorFlow and Caffe. HVX is integrated into Snapdragon processors and available as a standalone DSP, making it a powerful platform for bringing artificial intelligence to a wider range of devices and applications. Google Edge TPU The Google Edge TPU is a custom-built chip designed to accelerate machine learning workloads at the edge of the network. It works with TensorFlow Lite and is specifically designed for performing inference on low-power devices like IoT sensors and cameras. The chip can perform up to 4 trillion operations per second while consuming only a few watts of power. It can run pre-trained models for image and object recognition, natural language processing, and more. Google provides a software development kit and APIs for easy integration into applications. The Edge TPU is an energy-efficient solution for real-time inference and analysis of data at the edge of the network. TI Cavium CN99xx Thunder X2 CPU The TI Cavium CN99xx Thunder X2 CPU is a multi-core processor designed for data centre and cloud computing applications. It features up to 54 custom-designed cores, up to 3.0 GHz clock speed, and up to 1 terabyte of memory, with integrated hardware acceleration for encryption, compression, and virtualisation. The Thunder X2 CPU is optimised for high-performance computing workloads, supports virtualisation, and is compatible with various operating systems and standard server hardware components. Overall, it is a powerful and energy-efficient processor designed for high-performance computing applications in data centres and the cloud. LG Neural Engine The LG Neural Engine is a hardware-based AI accelerator chip that enhances the performance of LG’s smart devices. It can perform complex machine learning tasks and uses a combination of hardware and software, including deep learning algorithms. The Neural Engine can process data locally, without relying on cloud connectivity, and is energy-efficient to help extend battery life. It is integrated into LG’s proprietary operating system and works seamlessly with the device’s CPU to optimise performance and power consumption. Overall, the LG Neural Engine improves the user experience and enables faster and more accurate AI-driven features.
The LLM revolution has made chips that can handle AI at a large scale more important than ever
The LLM revolution has made chips that can handle AI at a large scale more important than ever
["AI (Artificial Intelligence)", "AI Chips", "Alphabet", "ChatGPT", "Deep Learning", "Google", "GPU", "IBM", "Machine Learning", "NVDIA", "Top Trend"]
["Top Trend", "NVDIA", "Amazon SageMaker", "computer vision", "deep learning", "GPU", "ChatGPT", "artificial intelligence", "machine learning", "AI", "neural network", "ML", "Machine Learning", "IBM", "generative AI", "Alphabet", "AI Chips", "Google", "Deep Learning", "AI (Artificial Intelligence)"]
["AI", "artificial intelligence", "machine learning", "ML", "deep learning", "neural network", "computer vision", "generative AI", "ChatGPT", "Amazon SageMaker"]
Shyam is a tech journalist with expertise in policy and politics, and exhibits a fervent interest in scrutinising the convergence of AI and analytics in society. In his leisure time, he indulges in anime binges and mountain hikes.