The Thermal And Power Realities Of The AI Era


The rapid growth of AI has created a surge in the global energy consumption at a rate never seen before. Today, data centers account for approximately 415 terawatt-hours (TWh) of electricity globally. To put this into perspective, the annual energy consumption of the United Kingdom in 2023 measured at 309 TWh. The International Energy Agency (IEA) projects data centers’ energy consumption wil... » read more

Detect, Diagnose, And Debug Using Sensors And Functional Monitoring


By Hari Mani, Henrique Mendes, and Robert Wilcox Modern AI workloads drive an extremely "spiky" power profile where current demands surge to hundreds of Amps within nanoseconds, clashing with the tighter operating ranges of advanced process nodes as they push below 0.8V. This creates a physical bottleneck: the on-die power delivery network (PDN) cannot sustain the instantaneous curren... » read more

Power, Not Area: Why Edge GPU Design Is Entering A New Era


For decades, semiconductor progress followed a familiar playbook: shrink the node, pack in more logic, raise the clock, and performance would follow. That model held remarkably well, and possibly much longer than it should have. As the industry moves below 2nm, GPU design is running into a hard physical reality. The limiting factor is no longer how much logic we can fit on a die. It’s how ... » 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

Spray And Pray Wastes Power


For quite some time I have felt that the way the industry approaches power is less than optimal. Techniques such as clock gating and power gating have been used to reduce the amount of unnecessary activity and leakage, but is there more activity that does not contribute to an intended action? While unnecessary activity may be unimportant in the functional sense, it all represents power that ... » read more

Same Chip, Two Destinies: How Power Profiles Improve With On-Chip Monitoring


What happens to critical power-related considerations when the same chip is handled two different ways, with or without visibility from within? This article begins by examining how the absence of on-chip monitoring impacts peak power, average power, and Di/Dt noise (rate of current change), as illustrated in the diagram below and the subsequent discussion. It then details how these aspects c... » read more

Will New Processor Architectures Raise Energy Efficiency?


Data centers continue to heat up as new processors consume more energy than ever before. Cooling is the primary weapon against the heat these processors generate, but it won’t be able to keep up forever with traditional processor architectures. New ones may be necessary. There are opportunities today to make well-known architectures more energy-efficient, but the number of options for subs... » read more

For Chip Developers, HW/SW Co-Design Key To Data Center Efficiency


Data centers and high-performance computing (HPC) are the primary enablers of today’s power-hungry AI-driven technology, but chip designers, EDA vendors, and the data centers themselves have a long list of options available to them to help curb AI's power consumption. Chip designers play a critical role in ensuring energy efficient processing from the bottom up, whether that is hardware-so... » read more

Scaling Performance In AI Systems


Improving performance in AI designs involves the usual tradeoffs in power and performance, but achieving a good balance is becoming much more challenging. There is more data to process, new heterogeneous architectures to contend with, and much higher utilization rates. Andy Nightingale, vice president of product management and marketing at Arteris, talks about where the bottlenecks are, how to ... » read more

The Cost Of EDA Data Storage And Processing Efficiency


Engineering teams are turning to the cloud to process and store increasing amounts of EDA data, but while the compute resources in hyperscale data centers are virtually unlimited, the move can add costs, slow access to data, and raise new concerns about sustainability. For complex chip designs, the elasticity of the cloud is a huge bonus. With advanced-node chips and packaging, the amount of... » read more

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