Milestone Innovations in Verification Engineering

🚀  𝑻𝒉𝒆 𝑨𝑰 𝑹𝒆𝒗𝒐𝒍𝒖𝒕𝒊𝒐𝒏 𝒊𝒏 𝑯𝒂𝒓𝒅𝒘𝒂𝒓𝒆 𝑽𝒆𝒓𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝑯𝒂𝒔 𝑨𝒓𝒓𝒊𝒗𝒆𝒅 As a verification engineer working on HBM3 interfaces, I'm witnessing firsthand how LLMs are transforming our industry. The traditional verification bottleneck - consuming 70% of development time - is being shattered by intelligent automation. 𝐖𝐡𝐚𝐭'𝐬 𝐇𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠 𝐍𝐨𝐰: ✅ LLM-generated UVM testbenches achieving 87%+ coverage automatically ✅ SystemVerilog assertions created from natural language specifications ✅ 20% improvement in verification outcomes with 15x faster setup times ✅ AI-driven coverage analysis identifying missed edge cases 𝗧𝗵𝗲 𝗚𝗮𝗺𝗲 𝗖𝗵𝗮𝗻𝗴𝗲𝗿𝘀: 🔹 UVM² Framework - First systematic LLM-driven verification automation 🔹 VERT Dataset - Open-source training data outperforming GPT-4o by 24% 🔹 Coverage-Driven AI - Iterative refinement based on real-time feedback 𝑭𝒖𝒕𝒖𝒓𝒆 𝑶𝒖𝒕𝒍𝒐𝒐𝒌: The verification landscape is evolving from manual, labor-intensive processes to AI-augmented workflows. We're moving toward: Natural language to HDL translation Autonomous bug detection and fixing Real-time verification strategy optimization Cross-platform verification portability 𝗙𝗼𝗿 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀(𝗙𝗢𝗥 𝗨𝗦): This isn't about replacement - it's about amplification. LLMs handle repetitive tasks while we focus on complex system-level verification and creative problem-solving. The future belongs to engineers who embrace AI as their verification co-pilot. The question isn't whether LLMs will transform verification - it's how quickly we'll adapt to lead this transformation. What's your experience with AI in verification? Share your thoughts below! #HardwareVerification #SystemVerilog #UVM #LLM #AI #Semiconductors #VerificationEngineering #TechInnovation #DV #semicons

*** UCIe and the Future of Chiplet Verification. *** UCIe (Universal Chiplet Interconnect Express) is aiming to do for Chiplets what PCIe did for expansion cards—create a standardized way for dies (chiplets) to communicate. But standardization doesn’t just mean easier integration. It also means: * New verification challenges. * New testing methodologies. * A new layer of complexity in system-level validation. Unlike traditional SoC verification, where all interconnect behavior is known in advance, UCIe introduces a mix-and-match dynamic where: * Chiplets from different vendors need to interoperate seamlessly. * Protocol verification must account for multiple implementations. * System-level validation has to consider unknown third-party chiplet behaviors. For verification engineers, this raises new questions: * How do we ensure compliance without access to third-party chiplet RTL? * How do we test performance across heterogeneous chiplets with varying latency and bandwidth? * How do we debug system-wide failures when components aren’t all from the same vendor? UCIe is a Step Forward—but it also introduces a whole new Verification paradigm. How do we adapt verification methodologies for an open chiplet ecosystem?

✨ 70% of chip design time goes into Verification. Now imagine AI cutting that time in half 😀 Working as a Design Verification Engineer at Google, I can clearly see how AI is rapidly reshaping VLSI. What once felt like a distant future is already becoming part of our everyday workflow. Today, the industry already has tools that can: 🔹 Generate RTL code directly from specification documents or architecture diagram. 🔹 Auto-create SystemVerilog/UVM testbenches from high-level inputs 🔹 Use ML to analyze coverage gaps and suggest corner-case tests 🔹 Assist in debugging waveforms and highlight potential root causes 💡 The big shift: Verification engineers will spend less time on repetitive coding and more on guiding AI, validating results, and applying domain expertise. Even in my own work, I don’t remember a single day in last month where I haven’t used some form of AI tools 🧠 Of course, the best AI tool really depends on what you need: some are great for coding, some are best for Circuit diagrams, while a few are better suited for documentation and writing. The key is to mix and match based on your requirement. Beyond popular tools like ChatGPT, Gemini, or Perplexity, here are some AI tools I’ve found particularly useful in Design Verification & VLSI : a) Claude AI – https://claude.ai/new b) Cursor AI – cursor.com/agents c) Bronco AI – https://www.bronco.ai/ 🚀 The pace of change is incredible. AI isn’t just “supporting” verification anymore – it’s starting to reshape how we design and verify chips. 👉 Curious to know in the comments: Which AI tools do you find most effective in your workflow? #VLSI #Semiconductor #Google

Verification can easily reach 50% of the cost and time of chip design so I am really excited to share our latest chip design breakthrough (led by Steven Herbst). Steven has leveraged his open source Switchboard framework to demonstrate a 1,300X build time spedup and 19X RTL simulation speedup (compared to stock Verilator) for a million core RISC-V based "waferscale" processor! We are proud of this work so we took the time writing up a proper paper and submitted it to a journal (available on arxiv as preprint). The paper goes through the theory and motivation that went into the development of Switchboard. Paper: https://lnkd.in/dCDfS-xQ Switchboard Sources: https://lnkd.in/danEwBMh

LLMs can now find bugs in your code by 'thinking' like formal verifiers. For years, program verification has relied on formal methods where requirements must be expressed in mathematically precise languages - but most real-world requirements are written in natural language. The gap between these two worlds creates a verification challenge as programs grow more complex. Researchers from Peking University and others have introduced HoarePrompt, adapting ideas from Hoare Logic (a cornerstone of formal verification) to work directly with natural language. Their approach generates natural language descriptions of program states at key code points, helping LLMs track how variables evolve throughout execution. For loops - the most challenging construct - they developed few-shot-driven k-induction to help models properly summarize loop effects. On a challenging benchmark of programming competition problems, HoarePrompt improved correctness classification accuracy by 62% compared to standard prompting techniques and 93% over test-based approaches. Understanding how programs evolve state is fundamental to program verification. We'll likely see major improvements in automated program understanding, debugging, and verification tools that don't require formal specifications. ↓ 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐤𝐞𝐞𝐩 𝐮𝐩? Join my newsletter with 50k+ readers and be the first to learn about the latest AI research: llmwatch.com 💡

Verification skills in AI era : AI has collapsed the skill floor for verification coding. Any engineer with a browser can produce correct SystemVerilog now. The syntax question is settled. It’s free. So what do you actually ask in a verification interview in 2026? The shift I keep landing on: the value of a verification engineer was never in writing the code. It was in knowing what to verify and why. Here’s what I’m actually screening for now: Coverage forensics: Don’t write me a covergroup — any LLM can do that. Instead, look at this 98% coverage report and tell me why I should still be nervous. The missing 2% might be the exact cross between low-power entry and outstanding coherent transactions that causes silent data corruption at a customer’s data center. The metric isn’t the skill. Reading through the metric is. Failure scenario thinking: You’re verifying a DMA engine sharing an AXI bus with a real-time safety processor. The spec says nothing about arbitration under back-pressure. What do you do? I’m listening for whether the candidate recognizes that the ambiguity is the bug — and has the instinct to build a test for something nobody asked them to test. Debug root-causing: AI can help you write tests. But when a simulation fails at cycle 4.2 million, can you trace it back through five levels of hierarchy to a single FSM corner case? Debug is pattern recognition built on years of watching silicon break. It’s the difference between someone who runs simulations and someone who actually finds bugs. Verification planning strategy: Knowing when to deploy formal vs simulation vs emulation vs FPGA prototyping — and why. A cache coherency protocol might need formal. A video pipeline might need emulation for real-frame throughput. AI can execute any of these. Signoff conviction: Would you hold a tapeout? When the schedule says go but your gut and your coverage holes say wait — do you have the spine and the data to make that call? MediaTek’s Dimensity, Apple’s M-series, Qualcomm’s Snapdragon — none of these tape out on syntax correctness. They tape out on whether the verification team had judgment and conviction. AI makes the average verification engineer more productive. It doesn’t make them more thoughtful. And in verification — where our job is imagining failures the design team didn’t — thoughtfulness is the product. If you’re still asking candidates to hand-code a UVM agent on a whiteboard, you’re testing a skill that costs $0 and takes 4 minutes. If you’re hiring for ASIC verification right now — what’s the one question you’ve added or dropped because of AI? Genuinely curious how the industry is adapting. #VerifWord #ASICVerification #Taalas #AIChips #VLSI #EDA #ChipDesign #FormalVerification #SemiconductorDesign #AIInference