Over the past decade, AMD has undergone a dramatic transformation under CEO Lisa Su’s leadership, evolving from a struggling semiconductor firm to a formidable competitor across data center, client computing, and embedded markets. The company’s embedded business unit has emerged as a particularly bright spot, with its adaptive computing portfolio and AI-focused strategy positioning AMD for significant growth in edge applications.
The 2022 acquisition of Xilinx proved pivotal, providing AMD with critical FPGA and SoC technologies that have since been deeply integrated with its x86 CPUs, GPUs, and NPUs. This strategic combination enables AMD to offer unique heterogeneous computing solutions, particularly valuable in automotive, aerospace, and industrial applications. During a recent analyst briefing, Salil Raje, head of AMD’s Adaptive and Embedded Computing Group, outlined a five-pronged strategy emphasizing adaptive computing leadership, developer experience, embedded CPU growth, custom silicon wins, and AI acceleration.
AMD’s approach stands in stark contrast to competitors like Intel, particularly in its flexible architecture strategy. Rather than locking customers into proprietary ecosystems, AMD supports multiple compute architectures including x86, Arm, GPU, and FPGA configurations tailored to specific use cases. This openness extends to AI software stacks, where AMD actively collaborates with ecosystem partners rather than enforcing closed solutions. The strategy appears to be gaining traction – while AMD currently holds just single-digit share in embedded CPUs, the company sees substantial growth potential as edge AI adoption accelerates.
The edge AI market represents AMD’s most promising opportunity. With predictions of a “ChatGPT moment” coming to edge devices, AMD is embedding NPUs across its product portfolio to enable low-latency AI inference in applications ranging from industrial automation to autonomous vehicles. Recent product launches like the Versal AI Edge Gen 2 and EPYC Turing 9005 processors demonstrate AMD’s ability to scale AI acceleration across performance tiers. The company’s software tools further strengthen its position by simplifying the transition from cloud-based AI training to edge deployment.
AMD’s custom silicon business, initially built around gaming consoles, is expanding into automotive and defense sectors through a disciplined approach focused on differentiated IP integration. The company’s leadership in chiplet architecture provides additional flexibility, allowing customers to mix AMD IP with their own designs in cost-effective configurations. This technical capability, combined with Lisa Su’s consistent execution-focused leadership, has enabled AMD to avoid many of the missteps plaguing larger rivals.
As Intel struggles with manufacturing delays and organizational uncertainty, AMD finds itself well-positioned to capture embedded market share. The company’s combination of architectural flexibility, power efficiency, and open ecosystem approach resonates strongly in edge computing environments where customization and latency sensitivity are paramount. While challenges remain from Arm-based competitors and increasing system complexity, AMD’s strategic focus and product execution suggest the company will play a defining role in shaping edge computing’s AI-driven future.
From near-irrelevance a decade ago to technology leadership today, AMD’s embedded business transformation mirrors the company’s broader renaissance. With its comprehensive edge computing platform and disciplined growth strategy, AMD appears poised to not just participate in but actively shape the next evolution of distributed, intelligent computing.
