Eric Qian

From rockets to AI. Nine years after the original NVIDIA DGX-1 handoff, Jensen Huang delivered a brand-new DGX Spark to Elon Musk at Starbase. The DGX Spark is a petaflop powerhouse built for creators, researchers, and developers — a desktop-sized supercomputer with five times the performance of the first DGX-1. 🚀✨ Filmed at SpaceX rocket factory in Texas, this moment captures a conversation between the two visionaries. ➡️ https://bit.ly/4o7oqGw 📹 https://bit.ly/42GxidS

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#reconfigurable_space_computing_lab at CPP: I’m excited to announce the launch of our new Ptah website—Secure Edge-AI & Post-Quantum Crypto in Space Systems—at Cal Poly Pomona’s Reconfigurable Space Computing Lab! 🚀🔐 Explore how Ptah empowers students to: Build resilient space-grade and terrestrial edge-AI platforms, combining RISC-V accelerators, Raspberry Pi clusters, and NVIDIA edge GPUs Integrate Post-Quantum Cryptography (CRYSTALS-Dilithium & Kyber) and lightweight AEAD (ASCON) to safeguard firmware, telemetry, and command streams Orchestrate heterogeneous clusters with K3s, monitor performance with Prometheus & Grafana, and deploy containerized microservices under real-world constraints Over a 15/30-week curriculum, learners dive into hands-on labs—benchmarking PQC schemes, configuring Kubernetes on Pi/Jetson nodes, and building self-healing drone and rover networks. You’ll find architecture diagrams, performance charts, code snippets, and video walkthroughs to guide every step. A huge thank you to Dr. Wen-mei Hwu (UIUC) for your mentorship, Dr. Benny Cheng (Naval Surface Warfare Center) for your partnership, and our incredible RSCL students whose creativity drives Ptah forward. 👉 Check out the site and let me know if you’d like to collaborate or integrate Ptah into your curriculum: https://lnkd.in/guKqU765 #EdgeAI #PostQuantumCrypto #ReconfigurableComputing #SpaceSystems #Education #Kubernetes

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How is NVIDIA worth $4 trillion? For anyone not in the industry, it might still seem bizarre that a company that started life making gaming hardware should become the most valuable company in history. But in the last year, I’ve been lucky enough to work with their amazing teams and get a sense of the tangible value behind that number. Their output in the last year has been incredibly impressive. To name just a few: > DGX Spark - the world’s smallest supercomputer - is already revolutionising the workflows of researchers and is a major step in democratising AI. > NVIDIA Blackwell GPUs started shipping in large quantities - with optimised memory capacity and bandwidth accelerating the AI revolution. > Details of the Rubin series due to launch next year. That’s not to mention a 200,000 GPU Colossus supercomputer, and a 500,000 GPU facility planned in the UAE, upgrades to networking and CPUs, and a whole host of other innovations. The company has done in a year what was once expected to take a decade. So yes, I’ve seen firsthand that every penny of that valuation is earned. It’s been great to be a part of that journey, and I cannot wait to see where the company goes next. https://lnkd.in/ecKQ-6R8

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❓ Do you believe Materials Science is everywhere? 10 years ago, a UC Berkeley materials science undergrad joined our lab. After a month reviewing corrosion papers, he quit — and went to a coding bootcamp. That moment stayed with me. Because hard science takes tremendous efforts and patience, progress rarely happens overnight. But when it does, it changes the world. Take Materials Science as an example, I would believe it is, quite literally, everywhere. From quantum materials to polymers, semiconductors, and beyond — Materials Science shapes the world we live in. And yet, its quiet brilliance often goes unseen. Let’s make this a live discussion, instead of chatting with ChatGPT... 👇 To all brilliant experts, please share one material and one application you know well or you are creating — in one sentence. I will gather them, to learn and for fun! A possible format: Application - Material + Core Function (highlight your company if you like). ➔ F-35 Fighter Jet – Radar-absorbent carbon/polymer composites that hide jet from radar (Lockheed Martin) ➔ Solar Cell – Perovskite film converting sunlight into electricity. ➔ Golden Gate Bridge – Zinc-rich primer shielding steel from salty fog. ➔ Sunscreen – Titanium oxide nanoparticles reflecting UV rays. ♻️ Friends, please repost to help gather more inputs and insights! #MaterialsScience #AdvancedMaterials #HardTech #Research P.S. Why this post? To remind us all that, in the era of AI, more people start replying on AI, yes, AI can certainly help, but human still need think and create harder (even slowly), which is vital for humankind evolution. Respect to Dr. Hitoshi Nakamura, check and learn about his battery experiments, you will know!

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🔥 "Sir, this synthesis is taking forever. We're already behind schedule." That's what my project partner said during our final year VLSI project last month. We'd been running synthesis iterations for 3 days straight, tweaking constraints, trying different optimization settings. Nothing seemed to work. Then our professor shared something that changed everything... "What if I told you there are EDA tools out there that can do in 2 hours what you've been struggling with for 3 days?" At first, I thought he was joking. How could AI possibly understand the intricate art of VLSI design that takes engineers years to master? Here's what I discovered that blew my mind: 🧠 The "Aha!" moment: While we were manually tweaking synthesis settings, Google's AI had already designed their TPU in 6 months (vs. the traditional 18 months). The AI didn't just optimize - it LEARNED from millions of successful designs. 🔍 The deeper I dug, the more I found: Cadence's AI tools predict timing violations before they happen Synopsys's machine learning algorithms suggest optimal floor plans Some companies report 60% reduction in design iterations The plot twist? This isn't about AI replacing us. It's about AI amplifying our creativity. My biggest realization: While I was worried about memorizing every synthesis flag, the industry was moving toward engineers who could think architecturally, solve complex problems, and work alongside intelligent tools. The wake-up call: That struggling 3-day synthesis project? With the right AI-enhanced tools, it could have been solved in hours, leaving us time to focus on innovation, creative problem-solving, and pushing the boundaries of what's possible. My takeaway as a future VLSI engineer: The question isn't "Will AI take my job?" It's "How can I become the engineer who shapes how AI transforms VLSI design?" Here's what I'm doing differently now: ✅ Learning Python alongside SystemVerilog ✅ Understanding ML fundamentals (not to become a data scientist, but to collaborate better) ✅ Focusing on architectural thinking rather than just tool mastery ✅ Asking "Why?" instead of just "How?" The story continues... Two weeks later, our professor introduced us to a new EDA tool with AI capabilities. Same complex design. 4 hours instead of 3 days. Mind = officially blown 🤯 Question for you: Have you experienced that moment when technology completely shifted your perspective? What was your "AI awakening" in engineering? P.S. If you're a VLSI student like me, don't just learn the tools - learn to think like the engineers who will define how these tools evolve. #VLSI #AIinVLSI #EDAAutomation #ChipDesign #VLSIStudents #EngineeringStories #TechTransformation #RTLDesign #SemiconductorIndustry #StudentLife #LearningJourney

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Over the past few weeks, I’ve been working on building a complete single-cycle RISC-V processor from scratch, entirely in Verilog HDL. After verifying the design, I reimplemented the same processor using Digital — a circuit design and simulation tool — to gain visual insight into its behavior. Then, to consolidate my learning and share the insights I gained, I also began writing a comprehensive document that clearly explains each step I took during the processor’s design — from instruction decoding and Datapath creation to control signal generation and memory handling. Finally, I’ve completed writing a detailed document that explains each step and every module involved in the design process. From instruction decoding and datapath construction to control logic and memory interfacing, the document breaks down the processor’s architecture in a clear, modular, and educational manner. 🔗 GitHub: https://lnkd.in/gb5gYPFR 🔗 EDA Playground: https://lnkd.in/gE2pmEFb  🔗 Digital Software Based Implementation (way cooler and you can easily see what is really going on inside the Processor!) : https://lnkd.in/gMZ5zMXB I tried my best to explain everything from scratch but still there are many rooms for improvements. However, this has been an incredibly rewarding project that helped me deepen my understanding of computer architecture, instruction set design, and digital circuit implementation. I could not perfectly add the Verilog Codes since they are very long, so if you are interested you could check my Github Page. Would love to connect with others working in the fields of hardware design, RISC-V, or Verilog-based systems! #RISCV #Verilog #ComputerArchitecture #HDL #ProcessorDesign #DigitalDesign #SystemVerilog #FPGA #VLSI #EDA #OpenSourceHardware

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Our Puget Systems review of the AMD Ryzen Threadripper 9000 processors is live! Evan Lagergren breaks down how their perform for professional content creation workflows in Premiere, After Effects, Photoshop, DaVini Resolve and more. Check it out: https://lnkd.in/gqviB6jJ

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