The introduction of large-scale language models ChatGPT and DALL-E 2 in 2023 brought generative AI to the public eye and an unprecedented level of excitement about generative AI. This makes chips capable of processing AI at 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 have the potential to capture 40-50% of the total market share. Companies like Alphabet, Broadcom, Intel, NVIDIA, Qualcomm, Samsung Electronics, and TSMC make chips used to train AI models. According to research, NVIDIA holds 88% of his GPU market.
As a result, many users see NVIDIA as a major beneficiary of the thriving generative AI domain. But there are other important players making their way into the space, such as Cerebras, Alphabet and IBM.
Here is a list of the top AI chips.
Jetson-NVIDIA
Jetson is Nvidia’s line of embedded computing boards designed to power AI and computer vision applications on edge devices. The Jetson platform includes products ranging from entry-level development kits to high-performance supercomputers. These boards feature Nvidia’s GPU technology, CPU and I/O capabilities and are optimized to run 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 software development kits (SDKs) and libraries such as CUDA and cuDNN to allow developers to build and deploy his AI applications on his Jetson.
Cerebras Systems WSE
The Cerebras Wafer Scale Engine is a specialized chip that accelerates AI workloads. It is a large single chip with 1.2 trillion transistors and 400,000 AI-optimized processing cores that work together to perform AI computations at unprecedented scale and speed. The chip’s unique design allows for easy integration into existing data center infrastructure. WSE has a successor called WSE-2, which offers 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 Inference
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 optimize the processing of large neural networks used for machine learning inference. AWS Inferentia is built with a large number of on-chip memory and processing cores, capable of performing numerous computations in parallel. This results in faster and more cost-effective inference performance for machine learning models in production. Inferentia integrates with his 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 his TensorFlow to allow developers to build and optimize machine learning models for his Inferentia.
IBM power 10
In August 2021, IBM announced Power10, a microprocessor. Designed to deliver high performance and scalability for enterprise workloads in AI, cloud computing, and hybrid cloud environments. Power10 has 18 billion transistors and is made using 7nm process technology. It comes with up to 15 processor cores that can run up to 8 threads simultaneously and can process 120 threads simultaneously. The chip’s advanced memory features include support for HBM2e memory, which offers four times the memory bandwidth of DDR4. In addition, it has new hardware-based security features that provide protection against cyberthreats, such as transparent memory encryption and secure boot. Power10 is a robust and flexible microprocessor that can meet the requirements of modern enterprise workloads, especially in AI and cloud computing.
Then, in mid-2022, IBM announced an expansion of its Power10 server line, introducing midrange and scale-out systems to power and automate business applications and IT operations.
Qualcomm Hexagon vector extension
Qualcomm Hexagon Vector Extensions (HVX) is a hardware platform developed by Qualcomm for mobile and embedded devices. 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 instructions optimized for machine learning workloads. It has a large number of vector registers and supports popular machine learning frameworks such as TensorFlow and Caffe. HVX is integrated into the Snapdragon processor and available as a standalone DSP, making it a powerful platform for bringing artificial intelligence to a wide range of devices and applications.
Google Edge TPU
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 to run inference on low-power devices such as IoT sensors and cameras. The chip can perform up to 4 trillion operations per second while consuming only a few watts of power. You can run trained models for image and object recognition, natural language processing, and more. Google provides software development kits and APIs for easy integration into your applications. 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
TI Cavium CN99xx Thunder X2 CPUs are multi-core processors designed for data center and cloud computing applications. It features up to 54 custom-designed cores, clock speeds up to 3.0 GHz, up to 1 terabyte of memory, and integrated hardware acceleration for encryption, compression, and virtualization. Thunder X2 CPUs are optimized for high-performance computing workloads, support virtualization, and are compatible with a wide range of operating systems and standard server hardware components. All in all, a powerful and energy-efficient processor designed for high-performance computing applications in data centers and clouds.
LG Neural Engine
LG Neural Engine is a hardware-based AI accelerator chip that boosts 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. Neural Engine can process data locally without relying on cloud connectivity, is energy efficient, and helps extend battery life. Integrated into LG’s proprietary operating system, it works seamlessly with your device’s CPU to optimize performance and power consumption. Overall, LG Neural Engine improves user experience and enables faster and more accurate AI-driven features.
