Prith Banerjee’s Post

NVIDIA and Ansys talking about leveraging ML/AI for design and automation at #DAC61. Some key insights shared by Prith Banerjee and Sanjay Choudhry 1. help sort out manufacture design at early phase 2. Basic physics as boundary foundation 3. Real time decision making in applications in Automotive and robotic 4. Purely physical driven AI has limits on local optimization instead of global: Purely data driven AI May have accuracy calibration issue. Hybrid can provide the sweet spot. 5. Domain adaptive training to create domain specific foundation model, and the further developing small model(SLM) for particular application, such as Chip Nimo, Coollitho, etc 6. LLM should not be expected to have all accuracy needed for specific domain application. 7. There have been quantum jumps for ML computation from hardware(chip+systems) Innovation such as nvlink. 8. Massive libraries and developers is a key to the success of CUDA+nvlink+GPU integrated stack; 9. Speeding up is the name of the game on having AI work with existing automation and simulation tools 10. When GenAI is applied in chip design, the new codes/new designs will be generated, RAG will improve accuracy and relavancy. 11. AI replacing chip designers? No, just like robotics not replacing surgeons, but it will change some natures of the jobs, at least with skills of utilizing AI.

History of AI is being made Jen-Hsun Huang, the founder of NVIDIA—the third-largest company in the USA—has just announced a groundbreaking collaboration with Ansys, Synopsys, and Cadence. Together, they are joining forces to forge a formidable generative #ai , focusing on creating GPUs capable of performing the computations necessary to bring about a new reality centered around creating digital twins. With annual revenues exceeding tens of billions of dollars, this partnership underscores a transformative shift in the world, emphasizing that "the last 1000 years of science have led us to this point." They aim to create "brilliant systems" by generating "multimodal data, going beyond just text." This marks a significant step forward for #nvidia and AI, notably by connecting to #llm , which allows them to follow said instructions, highlighting the profound implications for AI and LLM advancements. ♻️ Share this post if it’s valuable 💬 Join our AI Community on Discord 📌 Estimate your AI or LLM-based project

I think that this article by Sally Ward-Foxton captures really well the strategic importance of ST's Edge AI announcement : What was announced : - The ST Edge AI Suite a fully integrated MLops environment specifically designed for Edge AI / embedded AI. - Integration of all existing ST's AI tools into the ST Edge AI Suite to provide full interoperability between components and accelerate development and deployment - NanoEdge AI Studio, the flagship AutoML tool providing from the 2021's Cartesiam acquisition becomes the AutoML tool for the ST Edge AI Suite and is now completely free for unlimited STM32 deployment. Why it matters : It shows ST's strong commitment to embedded AI and reinforces the vision that with the right set of tools the development of Edge AI solution will be facilitated and accelerated. With the ST Edge AI suite, developers have the full freedom on how they want to use the Suite : Simply access one tool, bring their data and build a model, bring their own model and optimize it, get inspired by a very rich model zoo, live test on an online Board farm, use NanoEdge to easily produce ML libraries or use the Suite End to End at every single step of the process. In addition the newly announced ST ML core gives the ability to select any ST's target, MCU, MPU, sensor or automotive without unnecessary rework, The vision that all the tools come for free including NanoEdge AI Studio, a tool already adopted by several thousands of companies is a clear commitment to removing any barriers to adoption and accelerating the deployment of edge AI. #tinyml #edgeai #stmicroelectronics

The growing role of AI in semiconductor chip design for optimizing chip performance, automating design tasks and bridging workforce gaps. Read on to know the specific AI-driven tools and platforms offered by #Cadence to enhance it's chip design processes. #GenerativeAI #ChipDesign #EDA #Semiconductors #Performance

AI-driven EDA optimizations... #ReinforcementLearning #AI #EDA #Optimization #Power #Physical

Nvidia's AI Designer Assistant: Boosting Chip Creation with ChipNeMo Imagine an AI assistant that summarizes bug reports, writes design scripts, and answers your chip design questions like a pro. That's ChipNeMo, Nvidia's secret weapon for boosting engineer productivity by 5%! Here's the magic: Trained on 1 TRILLION words of data(Tokens): ChipNeMo gets a "liberal arts education" in chip design, then specializes with Nvidia's 30 years of internal data (24 billion tokens!). Combines chatbot, scriptwriter, and summarizer: Need help understanding a bug report? ChipNeMo condenses it into a single paragraph, even tailoring it for engineers or managers! ➡️ Accuracy is key: Retrieval-augmented generation ensures ChipNeMo backs its suggestions with real Nvidia data, not just making stuff up. Why it matters: Saves senior designers' time: Let ChipNeMo answer those repetitive questions, freeing up senior engineers for more complex tasks. ⌚️ Simplifies complex tools: ChipNeMo writes EDA tool scripts in a natural language, making chip design more accessible. 🪄✨ Faster bug fixes: Summarized bug reports mean quicker decisions and fixes, saving precious time and money. ⏱️ Nvidia's advantage: Their 30 years of data give ChipNeMo an edge over competitors. EDA companies just can't replicate that experience! What do you think? Could AI assistants like ChipNeMo revolutionize chip design? Share your thoughts in the comments! #ai #chipdesign #productivity #nvidia #innovation 10xEngineers NVIDIA S. Sohail H. Naqvi Namra Nasyr

My top 5 AI predictions for 2024!! 1)   4D video editing will see rampant adoption. With advantages like dynamic content adaptation, realism, immersion, AI-enabled 4D #videoediting will transform the gaming and film production industry. 2) RAG applications will elevate to RAG + Fine Tuning. With advantages like enhanced accuracy & trust, cost-effectiveness, versatility in applications, privacy concerns - Retrieval Augmented Generation (RAG) will find numerous applications. More importantly, RAG will be supplemented with fine-tuning to reduce #hallucinations and enhance domain-specific response. 3)  Gen AI will become mainstream. Productizing Gen AI will be the focus. Large enterprises with deeper investments will focus on the commercialization & productization of #generativeai applications and will do so in an economically viable manner. 4)  #quantumcomputing & chip manufacturing will explode and become more competitive. Developments in quantum bits (qubits), error correction methods, and quantum coherence are pushing the boundaries of what's possible with quantum computers. Companies are continuously innovating in chip architecture, including developing 3D chip designs and using new materials like graphene. 5)  AI litigations will increase, and new AI regulations will pile up. As #LLM's applications increase, their constraints and loopholes will become more apparent, leading to a class of litigations; however, AI regulatory bodies will try to catch up by introducing stringent regulations demanding #transparency and explainability of models. Which predictions top your list? #artificialintelligence #predictions #emergingtrends #futureisnow #ageofai #innovations #opensourcecommunity Walmart Global Tech Walmart INFORMS World Economic Forum

🚀 𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗡𝗲𝘄𝘀 𝗶𝗻 𝗕𝗶𝗼𝗹𝗼𝗴𝘆! 🧬 EvolutionaryScale has released ESM-3: Simulating 500 million years of evolution with a language model, a groundbreaking new model for understanding protein sequences and functions. 🔍 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗼𝗳 𝗘𝗦𝗠-𝟯: • High Accuracy: State-of-the-art predictions for protein structures and functions. • Scalability: Handles large biological datasets efficiently. • Speed: Faster processing and analysis for quicker insights. 🧪 𝗜𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: • Superior performance in benchmark tests. • New discoveries in protein functions and interactions. This is a significant leap in computational biology, providing powerful tools for scientific research. Note: This version is coming soon to NVIDIA BioNeMo, a generative AI platform for drug discovery. The full ESM3 family of models will soon be available to select customers as an NVIDIA NIM microservice, run-time optimized in collaboration with NVIDIA, and supported by an NVIDIA AI Enterprise software license for testing at ai.nvidia.com.

Embodied AGI in the physical world 🧠 Project GR00T, NVIDIA's new initiative to create a general-purpose foundation model for humanoid robot learning. The GR00T model will enable a humanoid robot to understand multimodal instructions, such as language, video, and demonstration, and perform a variety of useful tasks. GR00T is born on NVIDIA’s deep technology stack: -Simulated in Isaac Lab (new app on Omniverse Isaac Sim for humanoid learning) -Trained on OSMO (new compute orchestration system to scale up models), -Deployed to Jetson Thor (new edge GPU chip designed to power GR00T). 👉 Follow Ultan O. for more resources, innovations & updates