What’s Different About HBM4


Memory bandwidth is limiting the flow of huge datasets that are needed to train AI models. There is much more data to process, store, and retrieve, but the speed at which that data moves through high-bandwidth memory (HBM) stacks is significantly lower than the speed at which data can be processed. Frank Ferro, group director for product management at Cadence, talks about the new HBM4 standard,... » read more

Crisis Ahead: Power Consumption In AI Data Centers


AI data centers are consuming energy at roughly four times the rate that more electricity is being added to grids, setting the stage for fundamental shifts in where power is generated, where AI data centers are built, and much more efficient system, chip, and software architectures. The numbers are particularly striking for the United States and China, which are in a race to ramp up AI data ... » read more

TSMC: King Of Data Center AI


Large language models (LLMs like ChatGPT) are driving the rapid expansion of data center AI capacity and performance. More capable LLM models drive demand and need more compute. AI data centers require GPUs/AI Accelerators, switches, CPUs, storage and DRAM. About half of semiconductors are consumed by AI data centers now. This percentage will be much higher by 2030. TSMC has essentially 1... » read more

Enhancing AI Datacenter PSUs With Hybrid-Si, SiC, And GaN Power Devices


The rapid growth of artificial intelligence (AI) is driving an unprecedented demand for processing power in data centers, resulting in a surge in power demand at the rack level. With the existing data center rack sizes, the challenge is to deliver more power and efficiency in the same physical footprint apart from costs and cooling. To address this, Infineon has developed a range of hybrid powe... » read more

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