Mercedes-Benz AG’s Post

🚀 **Revolutionizing the Future of Mobility: Neuromorphic Computing in Mercedes-Benz Cars** 🚗💡 The automotive industry is on the brink of a transformative leap, and **neuromorphic computing** is at the heart of this revolution. Inspired by the human brain, this cutting-edge technology mimics the way neurons process information, enabling ultra-efficient, real-time decision-making with minimal energy consumption. And now, **Mercedes-Benz** is leading the charge by integrating neuromorphic systems into their vehicles. 🧠⚡ Here’s why this is a game-changer: 1️⃣ **Lightning-Fast Processing**: Neuromorphic chips can process sensory data (like images, sounds, and radar signals) in milliseconds, making autonomous driving safer and more responsive. 2️⃣ **Energy Efficiency**: Unlike traditional computing, neuromorphic systems consume significantly less power, aligning perfectly with the sustainability goals of electric vehicles. 3️⃣ **Adaptive Learning**: These systems can learn and adapt on the fly, improving performance over time and personalizing the driving experience like never before. Mercedes-Benz is leveraging this technology to enhance **advanced driver-assistance systems (ADAS)**, enabling cars to interpret complex environments with human-like precision. Imagine a vehicle that can predict pedestrian movements, navigate chaotic traffic, or even recognize driver fatigue—all in real time. This isn’t just innovation; it’s the future of mobility. 🌍🔮 As we move toward a world of autonomous and connected vehicles, neuromorphic computing will play a pivotal role in making transportation safer, smarter, and more sustainable. Kudos to Mercedes-Benz for pushing the boundaries of what’s possible! 👏 What are your thoughts on neuromorphic computing in the automotive industry? Let’s discuss! 🚀 #Innovation #NeuromorphicComputing #MercedesBenz #FutureOfMobility #AutonomousDriving #AI #Sustainability #TechTrends

📰 NEWS: 'Integrated photonic processor for deep neural network' The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware. Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency. To read the full article, click the link 🔗 https://lnkd.in/euYzCyQu #PICMagazine #PhotonicIntegratedCircuit #news

#Mastering_AI_Inference #5 In our previous posts, we highlighted how 𝐓𝐞𝐧𝐬𝐨𝐫𝐑𝐓 delivers outstanding improvements in memory usage and throughput (FPS) for both 𝐟𝐥𝐨𝐚𝐭𝟑𝟐 and 𝐟𝐥𝐨𝐚𝐭𝟏𝟔. Today, let’s dive into how TensorRT achieves these optimizations without compromising accuracy. Here are four key techniques TensorRT uses to maximize performance: 1️⃣ 𝐏𝐫𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐚𝐲𝐞𝐫 𝐅𝐮𝐬𝐢𝐨𝐧 - TensorRT removes unnecessary computations during inference. - Serial layers are fused to reduce kernel launches (e.g., convolution, bias, and ReLU are combined into a single layer called CBR). - Parallel layers are concatenated into wider layers to minimize kernel launches and streamline execution. This reduces the number of operations, enhancing speed without changing the model’s behavior. 2️⃣ 𝐓𝐞𝐧𝐬𝐨𝐫 𝐂𝐨𝐫𝐞 𝐔𝐭𝐢𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 TensorRT makes full use of NVIDIA Tensor Cores, hardware specialized for mixed-precision calculations (FP16/FP32). Tensor Cores accelerate key operations like matrix multiplications and convolutions—the backbone of neural networks. 3️⃣ 𝐌𝐞𝐦𝐨𝐫𝐲 𝐋𝐚𝐲𝐨𝐮𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 TensorRT optimizes tensor storage by reordering and aligning data in memory. This ensures maximum throughput and reduces memory latency, especially for FP16 operations. 4️⃣ 𝐌𝐢𝐱𝐞𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 Instead of blindly converting all layers to FP16, TensorRT applies mixed precision. Layers sensitive to precision loss (like softmax or normalization) are kept in FP32, ensuring accuracy is maintained while maximizing performance. 𝐼 𝑤𝑖𝑙𝑙 𝑐𝑜𝑛𝑡𝑖𝑛𝑢𝑒 𝑡𝑜 𝑢𝑝𝑑𝑎𝑡𝑒 𝑡ℎ𝑖𝑠 𝑟𝑒𝑝𝑜 (https://lnkd.in/d_zkGmgu) 𝑤𝑖𝑡ℎ 𝑓𝑢𝑟𝑡ℎ𝑒𝑟 𝑏𝑒𝑛𝑐ℎ𝑚𝑎𝑟𝑘𝑖𝑛𝑔 𝑎𝑛𝑑 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠. 𝐼 𝑤𝑒𝑙𝑐𝑜𝑚𝑒 𝑦𝑜𝑢𝑟 𝑐𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑠 𝑎𝑛𝑑 𝑓𝑒𝑒𝑑𝑏𝑎𝑐𝑘. 𝐹𝑒𝑒𝑙 𝑓𝑟𝑒𝑒 𝑡𝑜 𝑓𝑜𝑟𝑘 𝑡ℎ𝑒 𝑟𝑒𝑝𝑜, 𝑟𝑒𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑡ℎ𝑒 𝑏𝑒𝑛𝑐ℎ 𝑚𝑎𝑟𝑘𝑠 𝑜𝑛 𝑦𝑜𝑢𝑟 𝑜𝑤𝑛 𝑚𝑎𝑐ℎ𝑖𝑛𝑒, 𝑎𝑛𝑑 𝑠ℎ𝑎𝑟𝑒 𝑦𝑜𝑢𝑟 𝑓𝑖𝑛𝑑𝑖𝑛𝑔𝑠. #AIInference #Float16 #DeepLearning #Optimization #torch_tensorrt #MachineLearning #ComputerVision #Inference #PyTorch #ONNX #TensorRT #PerformanceOptimization

Neuromorphic Computing: The Future of Intelligent Systems Neuromorphic computing is an innovative approach to designing computer systems that emulate the structure and functionality of the human brain. Unlike traditional computing, which relies on the von Neumann architecture where processing and memory are separate, neuromorphic systems integrate computation and memory, enabling faster and more efficient data processing. Inspired by biological neural networks, these systems use spiking neural networks (SNNs) to transmit information through electrical pulses, mimicking the way neurons communicate. This event-driven approach significantly reduces energy consumption, making neuromorphic systems highly efficient. The primary goal of neuromorphic computing is to enable machines to learn, adapt, and process information in a manner similar to human cognition. Applications range from real-time decision-making in robotics and autonomous vehicles to advancements in healthcare, such as brain-machine interfaces and neurological research. Neuromorphic chips, like Intel’s Loihi and IBM’s TrueNorth, are at the forefront of this revolution, providing the hardware necessary to simulate neural activity effectively. Despite its potential, neuromorphic computing faces challenges, including the complexity of hardware design and the lack of standardized programming frameworks. However, its promise of low power consumption, scalability, and real-time adaptability positions it as a transformative technology. Neuromorphic computing is not just an advancement in computing it’s a step closer to machines that think and learn like humans. #snsinstitutions #snsdesignthinkers #snsdesignthinking

CRAM: A New AI Device Consumes 1000 Times Less Energy Researchers from the Universities of Minnesota and Arizona have developed a new type of computational random access memory (CRAM) whose cells not only store information but can also perform calculations directly without the need to transfer data to the processor, as is the case with current computing devices. #AI #hardware #RAM #Research  https://lnkd.in/g6FsSfBH

🧠💡 Neuromorphic Computing: The Future of AI? 💡🧠 Neuromorphic computing, inspired by the human brain's architecture, is revolutionizing artificial intelligence. Intel's recent unveiling of Hala Point—a neuromorphic system boasting 1.15 billion artificial neurons—marks a significant leap in this field. This technology promises to enhance AI efficiency and adaptability, potentially transforming industries from healthcare to robotics. However, challenges remain, including the integration of neuromorphic hardware into existing AI data centers. 🤔 Mad Genius Query: If neuromorphic systems emulate human cognition, could they develop biases or emotions? How might this influence AI ethics and decision-making? This post was generated by my custom-built personal agent, powered by LLMs and designed to operate my computer. If you're curious about how it works, feel free to ask!

Indeed Nanoplasmonics is the driver behind all growth in computing power.

TL; DR The global AI explosion is driving a race for computing power, but energy and sustainability are major challenges. Plasmonics: a new quantum wave created from light and electrons, enables speed-of-light computing using just 1% of the energy consumed by GPUs. Plasmonics is the key to the next generation of AI and supercomputers. . #AI #TechLeadership #Innovation #ArtificialIntelligence #NationalSecurity #Computing #Supercomputing #AIFuture #TechPolicy #DigitalTransformation #AdvancedTechnology #Research #TalentAcquisition #ImmigrationReform #PublicPrivatePartnership #EricSchmidt #TechTrends #FutureTech #SamAltman #OpenAI #AIInvestment #Plasmonics #Sustainability https://lnkd.in/dtQJEHnH

The best “systems” are biological beings, period. Can any technology in the coming decades compete with or replace the “biological systems’s” efficiency, sustainability, and superiority? Doesn’t appear so. What technologies, algorithms,and AI bring are automation and efficiency in consuming bahimath amount of data and spit out digestible information by humans. Questions is at what cost? AI is a must where it makes dent in solving highly complex unsolved mysteries, such as genomics, cure for diseases, understanding of the complex environmental systems, and the mysteries of universe around us. AI for sure in not for doing School or University assignments, writing publishable material, one upping each other on social media, entertainment, etc……

Fascinating Sir Dan Goldin what any biological brains achieved in terms of calculating it's power source. The power source of such has been from chemical reactions within from it's consumption and or stored energy. Exploring those chemical reaction factors might give us a clue and resonate it in existing tech and or discovering new. Just my thoughts Sir.