AI has transformed the semiconductor industry, impacting design, manufacturing and the global economy at large. In 2025, AI adoption “in at least one business function” is at 78% globally. Further, 88% of C-suite executives are keen to speed up AI adoption, moving beyond the discovery phase to build and scale organizational value. As adoption continues to rise, system designers are leaning on chip manufacturers and the greater semiconductor industry to bring forth scalable, secure and energy-efficient solutions. Wider adoption has, in turn, brought about increased demand for more powerful and specialized chips.
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Compute Power Challenge: The Race To Accelerate Data Movement
Exciting developments in AI inference—like chain-of-thought prompting, which breaks down large, complex questions and prompts into smaller steps mimicking human reasoning—are opening the gates to the highest quality answers and transparency in the logical inference process of AI.
DDR5 MRCD and MDB Product Brief
Download the product brief to learn more about the Rambus DDR5 Multiplexed Registering Clock Driver (MRCD) and Multiplexed Data Buffer (MDB).
Data Centers 2.0: How AI Is Transforming Operations
Rapid advancements in AI are becoming commonplace, driven by large language models (LLMs) that now exceed 1 trillion parameters. While these AI models are revolutionizing many industries, their increasing demand for computational power is driving the need for more specialized and higher-performance infrastructure.
Breaking Through The Generative AI Memory Wall
The term “memory wall” was first coined in the 1990s to describe memory bandwidth bottlenecks that were holding back CPU performance. The semiconductor industry helped address this memory wall through DRAM architecture innovations, rapidly increasing memory bus widths and data rates, and better process technologies that made faster memory and interfaces manufacturable. The torrid pace of innovation in AI processing has given rise to a similar bottleneck that has created an “AI memory wall.”
Addressing AI’s Insatiable Demand For Power
The growth of AI has been staggering, and applications are emerging across the industry that offer new generative AI capabilities powered by large language models. The impact of these AI 2.0 applications is broad and fundamentally alters the way we interact with computers. However, this improvement in capabilities and performance has also been accompanied by massive increases in power consumption.
