Summary of Edge AI Chips from Major Companies such as NVIDIA, Intel, and AMD

Edge AI chip is a module specially used to handle a large number of computing tasks in artificial intelligence applications in the edge computing environment. It is usually integrated into edge devices, allowing these devices to perform real-time data processing, analysis, and decision-making locally without the need to transfer data to the cloud for processing.
The emergence of edge AI chips has greatly improved the intelligence level of edge devices, making various application scenarios more efficient, flexible, and secure. At present, there are multiple well-known edge AI chip suppliers in the market, such as Nvidia, AMD, Qualcomm, Intel, etc. These companies are constantly launching new edge AI chip products to meet the needs of different application scenarios.

NVIDIA

Nvidia has a series of important products and layouts in the field of edge AI chips. The Jetson series is a representative of NVIDIA's edge AI chips. Jetson platform is a series of modules and development tools specially designed by Nvidia for edge computing, covering the main application scenarios of edge AI such as robots, autonomous driving, industrial manufacturing, and smart cities. These products combine NVIDIA's GPU technology and deep learning technology, providing powerful computing and AI reasoning capabilities for edge devices.
The Jetson series products include modules of different models and specifications, such as Jetson Nano, Jetson TX2, Jetson AGX Xavier, Jetson Orin, etc., which have different performance characteristics and applicable scenarios.
The Jetson Nano module is a compact AI computer with superior performance and power consumption, which can meet the needs of running modern AI workloads, running multiple neural networks in parallel, and processing data from multiple high-resolution sensors simultaneously. This makes it an ideal entry-level choice for adding advanced AI to embedded products.
The extended Jetson TX2 embedded module series provides performance up to 2.5 times that of Jetson Nano, while consuming only 7.5 watts of power. The Jetson TX2 NX is compatible with the Jetson Nano pins and external specifications, while the Jetson TX2, TX2 4GB, and TX2i have the same external specifications as the original Jetson TX2. The Jetson TX2i is considered an ideal choice for building devices including industrial robots and medical devices.
The Jetson AGX Xavier is a computer designed specifically for autonomous machines. This compact, energy-efficient module can provide hardware acceleration and high-speed I/O performance for the entire AI pipeline, allowing customers to apply new AI applications to the edge. The Jetson AGX Xavier is suitable for complex applications that require real-time processing and advanced AI capabilities, such as autonomous driving and intelligent manufacturing.
The Jetson Orin module has a computing power of up to 275 trillion floating-point operations per second (TOPS), which is 8 times the performance of the previous generation product. It is suitable for multiple concurrent AI inference pipelines, and can also provide support for multiple sensors through high-speed interfaces. This makes Jetson Orin an ideal solution for the new era of robot development.
These Jetson modules are typically equipped with Nvidia's GPUs and deep learning processors, supporting multiple deep learning frameworks and algorithms, allowing developers to build and deploy efficient AI applications on edge devices.

Intel

Intel has made significant research and progress in the field of edge AI chips. Recently, Intel and its subsidiary Altera announced the launch of new edge optimized processors, FPGAs and market ready programmable solutions at the Embedded Expo, committed to extending powerful AI functions to edge computing. These products will provide power for artificial intelligence edge devices suitable for industries such as retail, healthcare, industry, and automotive.
Intel's edge optimized processors include Intel ® CoRE ™ Ultra, Intel ® CoRE ™ And Intel Lingdong ® Processor series, as well as Intel Ruixuan ™ Independent graphics card (GPU). These processors not only provide powerful image classification inference performance, but also offer higher performance and flexibility for edge AI devices.
From Intel's official website, it can also be seen that Intel's products applied to edge AI include: Intel ® Strong to ® Processor, Intel ® CoRE ™ Ultra and Intel ® CoRE ™ Processor, Intel Inspiron ® Processors, etc. These processors are an important component of Intel's edge AI product portfolio, primarily targeting key areas such as retail, industrial manufacturing, and healthcare.
Like Intel ® CoRE ™ Ultra processors provide excellent performance centered on graphics processing and AI through efficient edge processors packaged in BGA; Like the edge oriented fifth generation Intel ® Strong to ® Scalable processors, utilizing built-in accelerators, hardware level security, and power optimization to improve energy efficiency for edge workloads, can improve performance to power ratios for applications in AI, edge, healthcare, industrial, retail, and energy fields.

AMD

AMD also has an important layout and development in the field of edge AI chips, and has been continuously launching new products in the past few months of this year. In February of this year, AMD announced the launch of AMD Embedded+, a new architectural solution that will bring AMD Ryzen ™ (Ryzen) Embedded processors and AMD Versal ™ Integrating adaptive SoC onto a single integrated board, combining embedded processors with adaptive SoC, can accelerate the launch process of edge AI applications.
In early April this year, AMD officially released the Ryzen embedded 8000 series processors, which are the first embedded products equipped with AMD XDNA architecture NPUs, specifically designed for optimizing workloads in industrial AI applications. The AMD Ryzen 8000 series embedded processor uses 4nm technology, AMD "Zen 4" CPU architecture, RDNA 3 architecture GPU, and XDNA architecture NPU, with an AI computing power of 39 TOPS, of which the NPU provides 16 TOPS.
Recently, AMD announced the expansion of AMD Versal ™ The Adaptive System on Chip (SoC) product portfolio introduces the all-new second-generation Versal AI Edge series and second-generation Versal Prime series adaptive SoCs, which integrate preprocessing, AI inference, and post-processing into a single device, providing end-to-end acceleration for AI driven embedded systems.
The second-generation Versal series devices balance performance, power consumption, board area, and advanced functional security with information security. Its new features and features support the design of high-performance edge optimized products for the automotive, industrial, visual, medical, broadcasting, and professional audio and video markets.

high pass

Qualcomm also has important layout and contribution in the field of edge AI chips. Its edge AI chip combines high performance, low power consumption and optimized AI processing capabilities, providing strong support for various edge computing application scenarios.
Qualcomm has launched multiple products in the edge AI chip field, such as QCS8550 and QCM8550, which are designed specifically for performance intensive IoT applications and integrate powerful computing power, edge side AI processing, Wi Fi 7 connectivity, and enhanced graphics and video capabilities. They provide support for high-performance IoT applications and assist in their rapid deployment, such as autonomous mobile robots and industrial drones.
Like Cloud AI 100, this is a chip used for edge inference, capable of performing over a million billion operations per second. Cloud AI 100 will be produced in the second half of 2020, providing edge computing devices with powerful AI reasoning capabilities.
In addition to the aforementioned products, Qualcomm continues to introduce new edge AI chips to meet the development needs of the market and technology. These chips have significant advantages in power consumption, performance, security and programmability, which enable them to play an outstanding role in various edge computing application scenarios.

Write at the end

The competition in the edge AI chip market is very fierce, and besides AMD and Nvidia, many other companies are also actively investing in the research and development and promotion of edge AI chips. Therefore, when choosing edge AI chips, it is necessary to comprehensively consider multiple factors such as performance, power consumption, cost, and technical support to choose the chip that best suits your application needs.