The Register41%
Cadence's AuraStack agent melds AI with HPC to speed PCB, advanced packaging design 74%
By Tobias Mann97%
7/15/2026, 10:30:00 PM
BS Summary: This article contains 25 faulty reasoning types, including Availability Heuristic, Hasty Generalization, and Ambiguity (Equivocation), with Attempt to Sell a Product or Service as the most egregious example at 31% saturation with 196 hits. Analysis detected 1,650 faulty-reasoning hits from 632 analyzed words, generating a BS Score of 67.5% and a BS Rank of 74% (4,208 of 16,191 articles). This article is worse (more manipulative) than 74.00% of the article peer group.
How AI will change the way scientific computing is done remains an open question.
One relies on ultra-precise double-precision mathematics, while the other is perfectly happy working with 4 bits.
On the surface, the two are diametrically opposed, two extremes of a spectrum we call high-performance computing (HPC) — and yes, whether you like it or not, AI is HPC.
However, the latest AI offering from Cadence Design Systems, one of the biggest names in industrial HPC, offers a glimpse of how high- and low-precision compute could not just coexist, but work together to solve bigger and more complex problems faster and with fewer resources.
Announced on Wednesday, Cadence’s AuraStack is an agentic AI system built to assist electrical engineers to design and test printed circuit boards (PCBs), or conduct advanced packaging design and testing — two tasks that have historically relied on highly precise simulations.
AI is definitely a big piece of what Cadence has built; however the company isn’t replacing these tools with hallucination-prone AI models.
Instead, AuraStack is a bit like Anthropic’s Claude Code or OpenAI’s Codex, but rather than writing, compiling, debugging, and running C or Rust in a sandbox, Cadence’s latest agent is designed to orchestrate its existing test and simulation suites.
“AI is amplifying the value of our engineering products and technologies,” Michael Jackson, CVP of Cadence’s system design and analysis division, told The Register.
In other words, the AI model — we’re told AuraStack integrates with a wide range of open and proprietary models — functions as a natural language interface capable of planning and orchestrating complex multi-step circuit design and testing workflows that run at higher precision using CPUs, GPUs, and other accelerators.
“For example, if I'm going to check and fix the IR reliability, I need to identify the power management components.
I need to create a simulation-ready power tree, and then I need to do the simulation, and then I need to provide feedback to the designer,” Jackson said.
Cadence's existing product stack already automates many of these processes.
The problem, Jackson explains, is that a PCB or package design often requires completing thousands of tasks throughout its development.
“Sixty-five percent of an engineer's day is spent navigating and dealing with a lot of these tasks,” he said.
By orchestrating that scutwork, Jackson claims that AuraStack can deliver a 15x boost to productivity by letting the designer focus on design and engineering decisions rather than the individual tasks.
These gains are enough that several large players in the electronics space, including Nvidia, have already signed up for the service.
Cadence isn’t just melding AI with HPC for chip design or advanced packaging.
The engineering software provider has built similar agents for digital and analog chip design.
The idea of using low precision compute to run AI models that orchestrate more precise single- and double-precision physics simulations isn’t new.
Nvidia is one of the biggest champions of this approach, which makes sense seeing as its GPUs aren’t limited to training and running AI models, even if that’s what most folks are buying them for these days.
Earlier this year, we explored how researchers at the Department of Energy’s Sandia National Laboratories used AI agents to develop and test new hypotheses.
They described the system as a self-driving lab.
However, those tests, while similar in concept to what Cadence is doing with AuraStack, didn’t use LLMs and instead used more mature architectures like variational auto-encoders.
But given the success of code assistants, it’s not hard to imagine agent harnesses similar to AuraStack being used to automate lab equipment, perform simulations, and then iterate on the results, enabling scientists to continue their research even after they’ve nodded off for the night.
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