DDN’s Post

The world of AI is evolving, but the high costs of GPUs and CPUs have been a significant obstacle, limiting broader industry access to this technology. The good news is that NeuReality is on a mission to make AI more affordable! Their NR1 system is all about efficient AI operations that lower these cost barriers. Curious? Check out this two-minute video where Moshe Tanach breaks down the economics of NeuReality's NR1 system architecture and explains how NR1 is changing the game in AI cost efficiency. #AI #techinnovation #neureality

Linear Scalability of your compute, Capital Expenditure and Operational Expenditure are the three pieces that determine your bottom line ROI and product margins. Artificial Intelligence is the technology .. Affordable Intelligence is your business viability !

You might've heard about Groq insanely fast LLM chip and inference API, but you might not have watched a truly insightful interview with their CEO Jonathan Ross. Not only you will better understand LPU vs GPU (CPU), but also know a lot more about where this AI industry is heading. I have watched a lot of AI CEOs/Founders, but Jonathan is definitely on the very top of that elite group. And remember to watch all the way to the end. https://lnkd.in/g9KiHuz6

Data needs to move in an AI supercluster at the same rate that it is moving between the GPUs. The only way to do this is with optical connectivity. Radha Nagarajan explains. https://mrvl.co/3ROMbp9 NextGenInfra.io

Wondering how context windows in the latest SOTA AI models suddenly increased tenfold? 🤔 Meet ring attention with blockwise transformers 💍 In this research, the team used a ring network of GPUs to achieve these 2 significant breakthroughs: 🧠 Overcoming memory constraints: Distributing the self-attention computation across multiple processors to allow sequences to be broken into smaller sets of data for training and inference. The team describes this as "near-infinite" because the size is multiplied by the number of processors allowing for immensely larger context relative to a single processor. Note - my limited interpretation of the paper is compute is distributed across actual processor devices, not processor cores, likely since these cores are optimized to split compute already. 📈 Linear scaling of context size: Distributing the self-attention computation and overlapping communication and computation in key-value blocks overcomes the quadratic scaling limitations of traditional attention mechanisms such that window size scales linearly with the number of devices. 🔗 Link to the research paper ➡️ https://lnkd.in/gWHUNes9 Image credit to DALLE #ai #transformers #contextwindow # memory #research

AI Breakthrough: LPUs Transform Contact Centers and Redefine Customer Service! What are LPUs? Specialised processors for language tasks. Efficiency: 10x faster, 90% less latency, minimal energy use. Key Features: Combines CPU & GPU strengths: Sequential and concurrent task handling. Enhanced AI-driven customer service: Speed and accuracy in interactions. Real-world analogy: Imagine a super-efficient barista who not only takes multiple complex orders simultaneously but also prepares them with lightning speed and precision, ensuring every customer is satisfied. Do you know how LPUs are transforming voice AI in contact centers and paving the way for the future of AI-driven customer service! Join the conversation: How do you think LPUs will impact the future of customer service? Share your thoughts below! #AI #Innovation #CustomerService #TechNews

Random Contrast Learning (#RCL) is a new style of machine learning that brings the power of #MachineLearning to #CPU based devices. Learn how the application of RCL to your ML workflows will allow for simple, fast, and accurate classification AI for CPU. 🚀📊 #AIforCPU #MachineLearning #Innovation #TechInsights #RandomContrastLearning #LuminaRCL #LuminaAI

jonathan ross founder of groq Claude ai executive summary of this youtube video ÷÷÷÷÷÷÷÷ Here is a summary of the key points from the text: The article discusses the competitive landscape in the artificial intelligence (AI) chip industry, with a focus on companies like Groq, Nvidia, Google, and others. It introduces Groq's founder, Jonathan Ross, who previously worked on developing Google's Tensor Processing Unit (TPU). The text explores the differences between various chip types, such as GPUs, TPUs, CPUs, and LPUs (Latency Processing Units), and their respective strengths and weaknesses in AI applications. The article highlights Groq's unique positioning in the market, offering both hardware and software solutions optimized for running pre-trained AI models efficiently, particularly in latency-sensitive applications. It delves into the technical details of Groq's chip design, which has achieved a Peta Op (quadrillion operations per second) capacity, and its focus on sequential processing rather than just parallel computation. The discussion also covers topics such as the importance of latency in user engagement, the challenges of software development for different chip architectures, and the potential business implications of LPUs in handling sequential tasks like language processing and strategy games. Additionally, the text explores broader philosophical and ethical considerations surrounding artificial intelligence, including the concept of artificial general intelligence (AGI), the nature of consciousness and subjective experiences, and the role of human intuition in decision-making processes. It draws parallels between AI decision-making and human intuition, using examples from games like Go and historical scientific discoveries. The article also touches on the future of AI and its impact on various industries, emphasizing the importance of understanding and effectively utilizing AI technologies. It discusses the competitive landscape and strategies of major players in the AI chip industry, as well as the potential for new business models and the significance of open source in ensuring AI safety and accessibility.

Terrific article in Nikkei Asia about EdgeCortix's innovative & efficient approach to delivering AI inference at the edge. Worth a read.

"EdgeCortix has designed its chips to process data in a more streamlined way, to reduce the number of times the chip needs to access memory..." says a recent Nikkei Asia article on the current landscape of AI in Asia. See EdgeCortix's innovative approach to the industry and hear from CEO Sakyasingha Dasgupta, PhD about how our chips are optimized for AI applications in "constrained environments," using only a fraction of the power required by GPUs for similar performance. https://hubs.li/Q02LBx4R0