Telcos are taking a focused, low-risk approach to AI by prioritizing deployments in OSS/BSS and enhancing data correlation through generative models, while maintaining a clear line of sight on ROI. …

AI compute isn’t one thing. It’s two. Under the umbrella of “AI workloads,” training and inference represent distinct computational worlds with different goals, hardware profiles, and economics. They often get …

It’s been a big week for AI infrastructure as a whole, but especially for semiconductors. Increasingly more companies are joining to race to power future AI data centers, while there’s …

For decades, compute has scaled faster than memory. Processors can execute more operations every year, but the speed at which data moves in and out of memory has lagged behind. …

The semiconductor industry is changing quickly, especially as it relates to AI. As AI workloads grow ever more demanding, old monolithic chips are giving way to new chiplet-based designs. But …

Artificial intelligence has reshaped the semiconductor industry, driving an endless chase for better performance and efficiency. But as transistor scaling slows and Moore’s Law fades, the gains from smaller nodes …

This week’s semiconductor developments highlight how AI’s hardware backbone is evolving from all sides, from high-bandwidth memory to cloud-scale infrastructure. Samsung’s unveiling of its next-generation HBM4 chips signals a renewed …

The memory wall is more of an issue than ever in AI workloads. How will it be fixed? As AI workloads scale, compute performance is increasing far faster than memory …

Is one kind of AI processor likely to reign supreme? GPUs have long been the workhorse behind much of the AI infrastructure buildup. But as AI needs have grown, specialized …

Edge AI silicon is getting much more capable, but how much will edge AI really handle? Artificial intelligence is moving outside massive data centers. As silicon evolves, AI processing is …