In June, we taped out CN101, the world’s first thermodynamic computing chip. We’re now sharing early bring-up results from the first thermodynamic ASIC, showing how a physics-based approach can enable stochastic, stateful, and asynchronous computation directly in silicon. Also on YouTube: youtu.be/iq3QshQevsc #ThermodynamicComputing #CustomSilicon #ASIC #CN101

Design verification still depends on engineers manually reading hundreds of pages of JEDEC specs and translating them into testable representations. We've released DRAMBench, an open benchmark for autoformalizing DRAM specs and the paper behind it, presented at the @iclr_conf VerifAI Workshop. Our approach introduces an intermediate formal layer of timed Petri nets that capture device states, commands, and timing constraints in a compact, executable model. From that single representation, verification collateral derives automatically. By Jan Ole Ernst, Dmitri Saberi, Derek Christ, Thomas Zimmermann, Rajath Salegame, Suhaas Bhat, Stanislav Levental, Thomas Ahle, and Matthias Jung, in partnership with @FraunhoferIESE. Both DRAMBench and DRAMPyML are open source, Apache 2.0. Read it here: arxiv.org/abs/2605.00058

At @iclr_conf in Rio? Join us for the Normal Silicon Social on Sunday, April 26 from 6:30 to 8:30 PM at the Copacabana Palace. Request an invite: luma.com/3ioi64wv #ICLR2026

Chip design verification consumes up to 70% of the engineering effort on a project, and a significant chunk of that time goes to one task: reading hundreds of pages of natural language specifications and manually translating them into formal, testable representations. We have been working with @FraunhoferIESE on a better approach. Today we are releasing DRAMBench, an open benchmark that measures how well AI systems can formalize JEDEC memory chip specifications into timed Petri net models. Read more: normalcomputing.com/blog/fro…

Normal Computing is sponsoring @iclr_conf 2026 in Rio de Janeiro. Our research team will be on-site presenting papers spanning thermodynamic computing, Normal's physics-based computing architecture, and the AI methods underpinning Normal EDA, our platform for silicon engineering. Come talk to the researchers behind the work. We'll share a preview of what we're launching live at the conference. Follow along for real-time updates in the ICLR app. Meet us at the booth: Thursday, April 23 – Saturday, April 25 9:30 AM – 5:30 PM daily

Our homepage refresh is live. Partnered with more than half of the top 10 semiconductor companies by revenue, we're sharing more about how Normal EDA works and where we're headed. normalcomputing.com

"Normal is one of the most difficult places to work, probably in the world, frankly. You're working at the frontier of multiple fields in an effort to push and create a new field forward." In the final chapter of Inside Normal, @FarisSbahi and @zaqqwerty_ai talk about the founding conviction, the recursive loop between Normal EDA and the Carnot hardware program, and what it takes to build at this intersection.

"Why not be part of a company that's actually part of doing the changing? You get to be part of what's happening. You get to actually change the world." In Chapter 03 of Inside Normal, Craig Churchill and Johann George talk about how business and engineering work together at Normal, what the hiring bar looks like, and why the semiconductor industry's design methodology is changing.

What does it look like to build the product that changes how the world's most complex chips get designed? In Chapter 02 of Inside Normal, Hanna Yip, Max Aifer, and Adam DeHovitz talk about building Normal EDA, our purpose-built AI platform for semiconductors: customer deployments, daily product work, and why EDA innovation is required to make entirely new chip architectures possible.

Took a stab at the Zen of Agentic Software: Explicit is better than virtual The agent should see what you see You should see what the agent sees Build systems the agent can observe, test, and repair Give the agent good tools Prefer working with text The best tool is often a CLI A CLI is just a sub-agent Tool output should be signal, not exhaust Failure should be loud enough to debug Success concrete enough to verify Leave room for surprise, not for confusion Rely on the model’s intelligence, not its knowledge Make knowledge available. Retrieve just in time Build memory so the agent does not relearn the world Memory helps. Memory lies. Design for both Garbage context in, garbage out Intent is the highest-signal context If a model can’t solve problem with perfect context, it can’t solve problem A longer prompt is not a substitute for data Prompting is always the first baseline If one agent is too slow, consider parallelism Verifiers are great, use more of them Do not sneer at glue. Enough glue, arranged well, becomes product

"In the US we're expecting to have a 49 gigawatt shortfall around 2028...2030 on a global scale". @NormalComputing Co-Founder @FarisSbahi on the future energy requirements for compute: "In the next few years we're going to hit this really hard wall when it comes to the energy requirements of compute". "We need to be thinking about next generation architecture from a somewhat radical point of view... exploring new kinds of architectures that would be considered unconventional". "Designing this kind of silicon and optimising with the level of complexity that silicon has... [is] something very difficult to do with legacy software that hasn't really changed in the last 40 years".

.@FarisSbahi and our CBO Craig Churchill joined @etnshow live to talk Normal's growing presence in Europe, our London hardware team, and what AI means for the future of silicon engineering.

At GTC, Nvidia CEO Jensen Huang highlighted a new equation: Revenue = Tokens per Watt × Total Available Gigawatts. Normal Computing CEO @FarisSbahi says this explains the industry’s push for energy-efficient and workload-specific chips: "Not that many years from now, rather than having a very small number of different chips running all of our workloads, we're going to have hundreds, if not thousands of different chips tailored to each workload and application in the data center."

The industry's answer to the AI power crunch is to find more power. Faris's view: finding more energy is not a durable strategy. The goal is to need dramatically less of it. @FarisSbahi and Craig joined @Furrier and @theCUBE at the @NYSE for their "AI Factories: Data Centers of the Future" series. youtube.com/watch?v=pStVzEX7…

Rethinking compute and rewiring plant immunity: Our March update is live🚀 Dive into this month’s updates, including: - The latest from ARIA, including funding, events, and RFPs - A deep-dive into @NormalComputing world-first thermodynamic computing chip - A Q&A with Philip Carella at @JohnInnesCentre, who is leading an opportunity seed project within our Programmable Plants opportunity space, working to rewire plants’ immunity to better fight pathogens - Recommended reads –from using AI systems to optimise protein production to social networks for AI Scientists Read the full piece and subscribe to get our monthly updates directly to your inbox: ariaresearch.substack.com/pu…

Normal Computing has raised $50 million in strategic funding led by @SamsungCatalyst, bringing total funding to more than $85 million. We build AI for the semiconductor industry and are developing a new class of computing hardware, using our software to design our own hardware IP. We're partnered with more than half of the top ten semiconductor companies by revenue through Normal EDA, our purpose-built AI platform. In parallel, we completed the tape-out of CN101, the world's first thermodynamic computing chip, targeting up to 1000x gains in energy efficiency for AI workloads. Investors include @GalvanizeLLC , @CelestaCapital , @drivecapital , Eric Schmidt's First Spark Ventures, Micron Ventures, Brevan Howard Macro Venture Fund, and @ArcternVC. We're hiring: careers.normalcomputing.com Full announcement: prnewswire.com/news-releases…

Our Head of AI @thomasahle ran agents autonomously for 43 days and built a full verification stack: simulation, UVM, formal, mutation testing. 580K lines. The full writeup covers what worked, what didn't, and why formalized problems with objective correctness signals may be uniquely well-suited to agentic AI: normalcomputing.com/blog/bui…

thrilled to share that i joined @NormalComputing as an ai engineer in their copenhagen office the company is not only building thermodynamic hardware with up to 1000x energy efficiency, but also an EDA product for building new hardware this is a future that i'm excited for!

my results on AI for autonomous progress: we ran codex claude for 43 days straight to build a System Verilog compiler/simulator normalcomputing.com/blog/bui…