For decades, semiconductor test floors have operated as high-stakes black boxes. Every test engineer knows the grueling routine: a lot finishes, the STDF (Standard Test Data Format) files finally drop, and someone opens a dense report the next morning, only to discover a drifting parameter, a stuck handler, or a tester that sat idle for … Read more
There are two major workloads that make up AI compute: training and inference. Training looks back. It digests a frozen body of past text, images and code, and distills it into weights. Inference looks forward. It takes those weights and, one token at a time, produces something that did not exist a moment ago. Same … Read more
For more than a decade, the AI hardware industry focused mainly on model training, while inference represented a smaller share of overall demand. That balance is changing as trained models are deployed widely in recommendation engines, content-generation systems, transcription and translation services, and autonomous devices. Industry estimates now place inference at most data-center AI workloads. … Read more
PreambleHardware-assisted verification (HAV) platforms, including hardware emulators and FPGA-based prototyping systems, are the two dominant methodologies for validating long workloads on complex System-on-Chip (SoC) designs before tape-out. HAV sits within a broader continuum of verification engines: simulation and formal methods are applied earlier, at the block and sub-system level, while post-silicon validation comes later. Historically, … Read more
Recently, I had the opportunity to speak with Johann Foucher, founder and CEO of Pollen Metrology, a French company whose journey offers a rare vantage point on how artificial intelligence is reshaping semiconductor manufacturing from the inside out. What makes Pollen Metrology’s story compelling isn’t just the technology itself, but the timing and conviction behind … Read more
When this piece first ran a year ago, VSORA was the quiet outsider, a company with a bold architecture and no Silicon Valley address. One year on, the thesis has held up better than even its author expected. In January 2026, Sandra Rivera, former CEO of Altera and, before that, a two-decade Intel veteran who … Read more
s that do the math. The difficult part was everything around them: the software everyone already uses, and the constant struggle to move data fast enough to keep those math circuits busy. Historical DNA of VSORA VSORA came at the problem from the opposite end. That reversal is the most interesting thing about the company. … Read more
Contrast 20 watts with a megawatt: The brain and the LLM aren’t in the same race. Every time a large language model (LLM) answers a question, it does something the human brain has done effortlessly for 300,000 years, but it does so by burning enough electricity to power a small neighborhood. The brain in our … Read more
Predictive, generative, and agentic AI are not successive generations. They coexist inside enterprise systems, each placing fundamentally different demands on the underlying compute infrastructure. The enterprise context layer A consumer chatbot relies primarily on pretraining weight memory and optional web search. An enterprise system must operate against information absent from pretraining: engineering specifications, financial ledgers, … Read more
For the past decade, the artificial intelligence landscape followed a remarkably simple recipe: if you wanted better AI, you built larger models, fed them more data, and added more GPUs. The formula worked, and it worked spectacularly. Every new generation of hardware enabled larger neural networks, and larger neural networks broadened their reach. What began … Read more