Scale Up, Scale Out Get a New Partner


Key Takeaways: Three AI data center scaling strategies are scale-up, scale-out, and scale-across. Scale-up is within a rack; scale-out is between racks; scale-across is between data centers. Each of the three uses a different interconnect strategy to optimize either latency or jitter. As today’s data center workloads — especially for AI and HPC — outgrow the physical, ... » read more

Chiplets And 3D-ICs Add New Electrical And Mechanical Challenges


Key Takeaways • Chiplets and 3D-IC architectures add new thermal-mechanical stresses that can affect the reliability of entire systems. • As chiplets are assembled into packages, defectivity targets become more stringent for each component in a system. • Traditional silos are breaking down, forcing design teams to address issues such as materials choices that previously were handled by... » read more

UCIe’s Major Technical Components Are Now In Place


Key Takeaways UCIe 3.0 doubles bandwidth and enhances manageability, addressing new use cases and following an annual update cycle since 2023. The growing demand for chiplet-based architectures in AI data centers is driven by the limitations of monolithic chips, making inter-chiplet communication and connectivity crucial. While UCIe was initially seen as feature-heavy, many of its ma... » read more

Minimum Energy Per Query


Key Takeaways Extracting heat from a chip faster is a short-term fix to a bigger problem. The longer-term challenge is how to reduce the amount of energy used per query. Data movement, guardbanding, and software inefficiency are key targets for the future. Heat is a serious problem within AI chips, and it is limiting how much processing can be done. The solution is either to... » read more

Balancing Training, Quantization, And Hardware Integration In NPUs


Experts At The Table: AI/ML is driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge devices such as PCs and smartphones. Semiconductor Engineering sat down to discuss this with Jason Lawley, director of product marketing, AI IP at Cadence; Sharad Chole, chief scientist and co-founder at Expedera; Steve Roddy, chief marketing officer at Qu... » read more

Addressing Critical Tradeoffs In NPU Design


Experts At The Table: AI/ML are driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge devices such as PCs and smartphones. Semiconductor Engineering sat down with Jason Lawley, director of product marketing, AI IP at Cadence; Sharad Chole, chief scientist and co-founder at Expedera; Steve Roddy, chief marketing officer at Quadric; Steven W... » read more

How And Why To Optimize NPUs


Experts At The Table: AI/ML are driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge devices such as PCs and smartphones.  Semiconductor Engineering sat down with Jason Lawley, director of product marketing, AI IP at Cadence; Sharad Chole, chief scientist and co-founder at Expedera; Steve Roddy, chief marketing officer at Quadric; Steven... » read more

Liquid Cooling Gains Traction In Data Centers


All electronics generate heat, and that heat must be removed to ensure those electronics don’t overheat. Moving air has been the predominant approach for decades, with liquid cooling limited to particularly intense computing workloads, largely in the supercomputing domain. With the rise in AI, data-center power density has grown to the point where liquid cooling is now seeing a larger buil... » read more

Will 2026 Be Dominated By AI?


Many opportunities and problems became highly interlinked in 2025, fueled by the historic growth in everything AI. But how close are we coming to breaking points, and what are people doing to mitigate them? That is the story that will unfold this year. AI's penetration into an increasing number of workloads is placing almost quadratic demands on compute, memory, interconnect, and the archite... » read more

Limited by Power


AI is seen as a massive computation problem, but that is not the case, at least with the way that the problem is structured today. It is a data movement problem. This not only limits performance but represents most of the energy consumption. In addition, the industry spends most of its time and effort making small improvements that optimize aspects of the existing architecture, when what is ... » read more

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