Gilson de Lima

Effortless Integration for Smarter Insights: CloudEagle.ai Meets Bitbucket Integrating CloudEagle.ai with Bitbucket takes just minutes and gives you: ✔ Simplified App Tracking: No more manual reports, get the app usage data you need, when you need it. ✔ Centralized Control: Manage your Bitbucket security from the CloudEagle dashboard. ✔ Proactive Monitoring: Catch unusual login activity before it becomes a problem. Ready for a smarter, more secure development workflow? Book a demo today - https://lnkd.in/gXAhChrt #saas #saasmanagement #toolstuesday Atlassian #jira #toolstuesday

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Back in May, I was invited by the local chapter of IEEE SSCS in Santa Clara to give a talk on how to use LLM and Gen AI for high-speed design. The video has been uploaded to YouTube. It is a good summary of how we are using LLM and AI for SI. https://lnkd.in/eUy2HDhB A few months later, Eric Schmidt gave a controversial talk on where he thought AI would progress in the next few years in a Stanford computer science class. He was a little too frank on telling students to steal TikTok IPs and why he thought Google would fail. https://lnkd.in/epMKkmvv Both talks seem to completely overlap on how to create domain knowledge dominance using advanced LLM. He gave examples on taking over TikTok and in chemical engineering, I was focusing on high-speed SI designs. But it can be applied to any other hardware design disciplines.  What we are pursuing, and he seems to suggest the same approach : 1) Use LLM with deep context window to scan through large volume of knowledge (text, video or sound), in my case for SI/PI we will be scanning large volumes of design specs for design domain knowledge 2) Use LLM agents to execute task based on the ingested knowledge (context to action), in my case, we will be initiating simulations based on design spec range and look for optimal solutions 3) Repeat the above with multiple chain of thoughts and store the observation to draw a conclusion, in my case, after scanning through the design specs and obtain optimal spacing for performance, generate routing and constraint rules 4) We also add an optimization step which is necessary for engineering  5) Improve the LLM agent performance using an adversarial agent to critique the LLM agent's performance 6) We also add using fine-tuning and transfer learning to future proof the domain specific models. I apologize both are long talks ~1 hour.  I hope this will be a good starting point to discuss the use of LLM and Gen AI for SI/PI. Feedback and comments are most welcome.

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Discover the benefits of the Cerebras AI Model studio: - Train 1-175 billion parameter models 8x faster than most other publicly available GPU instances - Enable higher-performing models with longer sequence lengths (up to 50,000!) - Train your models from scratch or fine-tune open-source models with your data Learn more about our partnership with Cerebras Systems: https://hubs.la/Q02tw4KY0

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Curious to see how #DiffusionModels enable the AECO? Dive into our latest blog post to learn about how diffusion models can transform your AECO workflows with high-quality visualizations to rapid prototyping. https://bit.ly/3SasEiZ

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With NVIDIA NIM and NIM Agent Blueprints, medical researchers across the public sector can jump-start their adoption of state-of-the-art, optimized AI models to accelerate their work. 🔗 https://nvda.ws/3NnkMYy Learn how #NVIDIAInception startups Abridge & ConcertAI leverage NVIDIA AI solutions to improve efficiency, reduce administrative burdens, and advance medical research and patient care. 🩺 Abridge ConcertAI

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We are all trying to keep up with the pace of #AI #innovation - well, here's a free course from Andrew Ng and Vasudev Lal to help you build LLM and Vision #GenAI pipelines. #IamIntel Cool stuff!

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The 1st deadline for the NVIDIA Academic Grant program is in 1 week. We're accepting proposals for #GenAI #LLMs and Simulation & Modeling. You mustbe a full-time faculty member at an accredited academic institution that awards research degrees to PhD students. NVIDIA will be funding proposals for innovative work related to generative AI and large language models (LLMs), including multi-modal models and content generation, such as diffusion models. Projects should incorporate pretrained models from ai.nvidia.com or use NIMs, Modulus, TensorRT(-LLM), NeMo, NeMo Retriever, NeMo Data Curator, BioNemo, NeMo Guardrails, or Isaac Sim/Lab. Gen AI & LLM Proposals should be attached to one of more of the following themes: Techniques for Training, Customizing, and Accelerating GenAI Exploring New Approaches and Architectures for LLMs and GenAI or Foundational Models and Scientific Applications Using GenAI Simulation Proposals should be attached to one of more of the following themes: Simulation and modeling that incorporate Modulus, Omniverse, HPC SDK, cuNumeric, cuQuantum, or CUDA Quantum. Proposals should be attached to one of more of the following themes: Computational Fluid Dynamics Physics-Informed Machine Learning Quantum Computing Scientific Simulation Selected Principal Investigators (PIs) may receive Up to 32,000 A100 40GB hours or equivalent OR up to 8 RTX 6000 Ada

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Working on deploying ONNX models for performance-critical applications? Try our NVIDIA Nsight Deep Learning Designer ⚡ A user-friendly GUI and tight integration with NVIDIA TensorRT that offers: ✅ Intuitive visualization of ONNX model graphs ✅ Quick tweaking of model architecture and parameters ✅ Detailed performance profiling with either ORT or TensorRT ✅ Easy building of TensorRT engines Learn more and download ➡️

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Learn how #NVIDIAInception startup Transcripta Bio uses NVIDIA technology, including the MolMIM model, to enhance molecular property refinement and lead optimization in their drug discovery processes.

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Do you know what it takes to design a differential pair that works past 30 GHz? If you are planning to take on PCIe 4, 5, or 6, USB 3.2, or any modern SERDES interfaces, I hope so! When we were building the JITX autorouter we wanted to be sure to address the issues plaguing modern designers. We found guidance in this DesignCon paper from Donald Telian, Kevin Rowett, and Ilya Teplitsky: https://hubs.ly/Q02wf7l70 Summarizing the key points for diff pairs here: 1. Above 8 GHz: have fear. Above 30GHz, everything is a stub. 2. Route as short as possible to minimize loss 3. Match P/N statically to within ~0.15ps 4. Match P/N dynamically to never accumulate 0.5ps of skew within a 100ps window 5. Size skew matching bumps below the relevant feature size so they become 'invisible' 6. Space out skew matching bumps irregularly to avoid impedance discontinuities. (the paper has a lot of other great lessons, so check it out) We make sure that JITX helps people design better boards, so when we asked ourselves "How should our autorouter draw differential pairs?" our answer is of course "Just do the Best Possible Thing." Put the computer to work making the detailed shapes so humans can focus on the more important stuff. Here's a video of the router matching skew to 1 femtosecond, enforcing dynamic phase matching, minimizing distance, minimizing impedance discontinuities, and avoiding obstacles. Feels good to offload all that to the computer! You can learn more on our website: https://hubs.ly/Q02wfdCL0 What do you think about this paper Robert Feranec, Benjamin Dannan, John Burkhert? Agree or disagree with the approach? #electricalengineering #signalintegrity #DesignCon

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A significant advance that will help ease 3D design workflows https://lnkd.in/gk2bEr5W 3D mesh creation is a part of artistic work, architecture, mechanical engineering, and a lot more. A significant time goes into modeling. this new tool from NVIDIA will help reduce some of that

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