AI is rocketing ahead. It is the biggest industrial revolution of our age. AI adoption is growing, but still most are at ...
AI scalability will require full-stack co-optimization, not just bigger data centers. AI workloads require a 10X compute ...
Ethernet auto-negotiation; multiphysics to avoid overdesign; PCB design reuse; mobile LLM quantization; modeling BSPDNs.
At the recent Data Center World 2026 in Washington, D.C., one message came through louder than ever: AI infrastructure is ...
We nod at it, we put it on slides, and we move on. But the goalposts keep moving. Things that used to live comfortably at the ...
AI data centers need power from a range of sources, including batteries, to safeguard against blackouts, transient voltage spikes, and grid demand spikes. As with regenerative braking and ...
ChipAgents has introduced Renoir, an agentic large language model (LLM) whose name means “renew.” In early chip design ...
A new technical paper, Agentic Hardware Design as Repository-Level Code Evolution, was published by researchers at Nvidia ...
In next-generation silicon, AI can interpret system behavior at scale, but only if observability is designed into the fabric ...
Non-Volatile Memory Express (NVMe) has become the dominant protocol for high-performance storage across client SSDs, enterprise drives, and hyperscale data centers. With NVMe 2.0, the specification ...
DSP adoption demonstrated that technical innovation alone is not enough. Three lessons remain particularly relevant for edge ...
This is what the software-defined vehicle looks like in practice. Fewer chips, more consolidation, and far more dependence on ...
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