AMD 嵌入式业务高管在首尔受访称将以 FPGA/自适应 SoC 进军韩国“物理AI”市场，未披露任何订单、金额或新签约客户

AMD is stepping up its push into Korea’s physical artificial intelligence (AI) market with adaptive and embedded computing platforms. The company aims to expand from semiconductor equipment to vehicles and robots by leveraging heterogeneous computing and an open ecosystem.
Salil Raje, senior vice president and general manager of AMD’s Adaptive and Embedded Computing Group (AECG), said in an interview at AMD AECG’s Seoul office in Yeouido on Sept. 2 that Korea is a strategic physical AI market for the company because of its strengths in industrial automation and automotive technologies.
“Physical AI can help Korean manufacturing in many ways as the country faces a shrinking population and declining productivity,” Raje said. “Cooperation with the Korean government and companies can be a win-win.”
Physical AI is a next-generation AI technology that senses the physical world, makes judgments and decisions, and takes direct action. It can be used in healthcare, medical applications, drones and automobiles. Examples include AI robotic arms, unmanned aerial vehicles, autonomous driving (AD) vehicles and autonomous factory machinery. The technology goes beyond understanding information to perceiving and acting within physical environments.
AMD views physical AI not as a distant-future technology but one that has already arrived. “Many products in the real world are already using AI in various ways,” Raje said. “AI is becoming deeply integrated across embedded systems, with even traditional machine-control algorithms being replaced by AI algorithms.” Embedded systems are dedicated hardware and software built into devices or equipment to control and perform specific functions, a product area overseen by AMD’s AECG.
◇ AI Automation for Semiconductor Equipment
Korean semiconductor manufacturers including Samsung Electronics and SK hynix are using AI algorithms to automate manufacturing processes. Equipment uses AI to automatically perform key settings and operations, shortening process times and supporting unmanned operations. It can also reduce variations in process yields caused by differences in operator skill levels.
AMD is contributing to AI automation with heterogeneous computing solutions including field-programmable gate arrays (FPGAs) and adaptive systems-on-chip (SoCs).
An adaptive SoC is a heterogeneous semiconductor whose internal hardware circuitry can be reconfigured even after the chip has been manufactured and installed in a product. It combines components including a central processing unit (CPU), FPGA and AI engines. Similar to the human nervous system, the components divide workloads among themselves to maximize performance and power efficiency.
“We are continuing to work with Korean semiconductor equipment companies on process equipment automation using AI-focused FPGA and adaptive SoC product lines,” Raje said. “We are deeply involved in front-end semiconductor processing, back-end packaging and memory test equipment.” He did not identify the equipment companies involved.
Traditional machine-control algorithms were rule-based. Machines made decisions and operated according to predefined, uniform criteria, resulting in higher error rates. For example, rule-based automated inspection equipment can fail to identify irregular, non-standard defects, potentially leading to overkill, in which good products are incorrectly classified as defective.
◇ Open to Partnerships With Samsung, LG and SK hynix
AMD is strengthening cooperation with Korean companies beyond industrial automation into automotive and robotics, including humanoid robots. It is already working with StradVision and Nota AI. Autonomous-driving perception software developer StradVision is optimizing its high-performance multi-vision perception software for AMD hardware. Nota AI is jointly researching AI model compression and optimization for AMD hardware.
“Cooperation with major Korean companies such as Samsung, LG and SK hynix, which can deploy AMD’s various solutions across a broad range of applications, is extremely important,” Raje said. “Recent robotics investments by Korean companies represent a tremendous opportunity for AMD, and we remain open to working with any company.”
Both vehicles and robots require real-time perception of their surroundings, ultra-low-latency control and high levels of safety. Even a brief delay in system decision-making can lead to serious accidents.
◇ Heterogeneous Single-Chip Architecture Enables Low-Latency Physical AI
AMD’s heterogeneous semiconductor technologies, including adaptive SoCs, handle the sensing, decision-making and action stages of physical AI within a single chip. This minimizes data movement and helps prevent bottlenecks and latency. Products and platforms supporting this approach include the Ryzen AI Embedded 100 Series, Versal AI Edge Series and Vitis unified software platform.
The Ryzen AI Embedded 100 Series consists of the X100 and P100. The products integrate an x86-based CPU, high-performance integrated GPU and neural processing unit (NPU) into a single SoC. The CPU manages and coordinates the overall operation of physical AI. The GPU analyzes large volumes of data collected by cameras and other sensors and processes visual information for AI workloads. The NPU handles repetitive AI inference at low power. Integrating the three functions reduces data communication time and improves power efficiency.
The Versal AI Edge Series combines Arm-based CPUs, FPGAs and AI engines in a single SoC. The Arm CPU handles sequential and irregular computing workloads. The FPGA can immediately implement hardware interfaces tailored to the sensors used by a system while handling motor-joint synchronization and high-speed signal processing. The AI engines provide lightweight, high-speed acceleration for tasks including visual perception, inference and wireless communications signal processing. The platform delivers up to three times the compute density and up to three times the AI compute efficiency, measured in tera operations per second (TOPS) per watt, compared with competing products.
The Vitis unified platform is a hardware and software development environment for developing and validating AMD heterogeneous-architecture hardware in real time. Controlling FPGAs or adaptive SoCs traditionally required hardware-level design methods such as register-transfer level (RTL) and hardware description languages (HDLs). Vitis, by contrast, allows heterogeneous cores to be programmed together using higher-level C and C++ languages. Raje oversaw the platform’s development and launch.
◇ Open Ecosystem Offers Greater Flexibility
AMD also operates a Robotics Partner Network, a global collaborative ecosystem designed to help partners assemble and develop robots more quickly and easily, similar to building with Lego blocks. AMD provides standardized, high-performance technology platforms and validates boards and modules developed by external partners. Robot manufacturers can combine compatible, validated components and modules to accelerate product commercialization.
Unlike competitors’ closed, vertically integrated lock-in approaches, AMD provides greater development flexibility. Partners can redesign systems-on-module (SOMs) to suit their requirements. A SOM is an embedded computing module that integrates core computing functions including processors, memory and power management onto a compact circuit board. AMD also provides technical and marketing support to partners participating in its ecosystem.