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Neuromorphic Computing Market Report 2023 By Key Players, Regions, Competitive landscape and Forecast Till 2028 Neuromorphic Computing Market Size, Share & Trends Analysis Report By Deployment (Edge Computing, Cloud Computing), By Offering (Hardware, Software), By Industry Vertical (Automotive, IT & Telecom, Aerospace & Defense, Industrial, Healthcare, Others), By Application (Image Recognition, Signal Recognition, Data Mining, Others), COVID-19 Impact Analysis, Regional Outlook, Growth Potential, Price Trends, Competitive Market Share & Forecast, 2022 - 2028 🌐IMIR Market Research Pvt. Ltd. Global Neuromorphic Computing Market size was valued at USD 30.74 Million in 2021 and is projected to reach USD 8843.36 Million by 2028, growing at a CAGR of 83.2% from 2021 to 2028 according to a new report by Intellectual Market Insights Research. 📚 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲 𝘀𝗮𝗺𝗽𝗹𝗲 𝗥𝗲𝗽𝗼𝗿𝘁:👇https://lnkd.in/d6XMuji4 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭👇 Applied Brain Research Applied Materials Brain Corp BrainChip RainGrid, Inc. Brain-Machine Interface Systems Lab Brainwave Technologies CEA-Leti CognitiveScale Cortical.io Deep Vision Consulting Entropix, LLC General Vision Services. Graphcore. GreenWaves Technologies HACARUS Hewlett-Packard Development Company, L.P. HRL Laboratories, LLC IBM ICRON | Planning and Optimization Solutions Intel Corporation KNOWME Koniku. Lattice Semiconductor MemComputing, Inc. Merck KGaA, Darmstadt, Germany Mythic Neurala NeuroBlade Neuromorphic LLC Neuronic.ai NNAISENSE Numenta #computing #technology #tech #computer #b #computerscience #business #gaming #software #engineering #cloud #bhfyp #internet #networking #android #communication #computers #electronics #programming #automation #technological #pc #manufacturing #technews #industry #robotics #instatechnology #vintagecomputer #technical #data

https://lnkd.in/dKv_Rwtj A new approach developed by researchers at the University of California can double the processing power of existing devices without any addition of hardware, which could reduce the energy consumption of devices by half. Every computing device use built of many different components and systems like different types of memories and processors. Recent years brought the advances of AI and machine learning, which brought with them the addition of hardware accelerators, signal processing units, and other components. Each unit in such devices processes information individually and then moves it to the next unit, and so this movement of information from one component to the next creates a bottleneck that increases computing time and energy. A new experimental approach called Simultaneous and Heterogenous Multithreading (SHMT) was developed by Hung-Wei Tseng, an associate professor of electrical and computer engineering at the University of California – it addresses this problem by removing the sequential processing of information and allows simultaneous information processing. When tested using a multi-core ARM processor, an NVIDIA GPU, and a Tensor Processing Unit hardware accelerator, the SHMT framework delivered a 1.96 times increase in processing speed and a 51 percent reduction in power consumption. According to Interesting Engineering, this innovative information processing approach has immense implications. It would double the output of currently available devices without the need to upgrade the hardware, which so far had to be periodically upgraded to keep up with the performance of current technologies. Now, a mere change in how data is processed on a tablet or smartphone can unleash almost twice the computing speed and significantly lengthen the device’s lifetime. This approach will save money both for individual users and for technology companies with massive data centers that consume huge amounts of energy. The International Energy Agency estimates that these facilities are consuming nearly one percent of global energy demand, which is only expected to increase as we lean on more and more technology for our daily tasks. Furthermore, most facilities are powered by fossil fuels and are therefore major contributors to global carbon emissions. SHMT can halve energy consumption and help reduce carbon emissions. While this innovation is far from ready for commercial use, it brings new hope to a world gradually consumed with technology. #technews #ai #ia #bigdata #hardware

Exploring Opportunities in Embodied AI Embodied AI is at the forefront of innovation, combining advanced machine learning models with physical devices to enhance interactions. This technology is poised to reshape industries such as robotics, autonomous vehicles, and IoT devices, fostering smarter and more intuitive solutions. In tackling the challenge of deploying compute-intensive AI models on edge devices, researchers and engineers can focus on key optimization strategies and skills: 🔧 Chip Design for Edge Inference Developing AI inference chips optimized for real-time processing is crucial in the era of edge computing. Skills like hardware design, VLSI, ASIC/FPGA design, digital/analog circuit design, and microarchitecture optimization are essential for reducing latency and enhancing data processing efficiency at the edge. Leading companies in this space include NVIDIA , Qualcomm , Intel Corporation , Tesla , Apple , and Google . ⚙️ Compiler Optimization Engineers proficient in compiler optimization play a vital role in integrating foundation models for specific hardware device . Their expertise enhances code performance and resource management, enabling the execution of complex AI models on specialized hardware. Key skills include Parallel programming like CUDA, LLVM and high-performance computing, with companies like NVIDIA , Intel Corporation , ARM, IBM, Microsoft , and Google leading the way. 🖥️ Firmware and Embedded Programming Firmware engineers have the opportunity to create adaptive embedded systems that leverage foundation models for seamless hardware-software interaction. Proficiency in embedded systems, C/C++, RTOS, microcontroller programming, low-level hardware interfaces, and optimization for constrained environments is essential. Companies like Bosch , NVIDIA, NXP Semiconductors Semiconductors, Texas Instruments, and Qualcomm are pioneers in this domain. 🧠 AI Model Development and Optimization Researchers can explore optimization methods such as transformers, flash attention , distillation , quantization, and pruning to enhance AI models. Skills in deep learning, frameworks like pytorch , neural network , and model compression techniques are key, with industry leaders including Meta, Google DeepMind , OpenAI , Hugging Face , Microsoft Research, and Stanford University paving the way in AI innovation. As AI foundation models develop at a fast pace, it's crucial to further improve on the above areas to utilize them efficiently. This post is an high-level overview and I hope it gives you insight into the vast landscape of running foundation AI models on edge devices. #AI #MachineLearning #Optimization #EdgeComputing #iot #robotics #autonomousvehicles #embeddedsystems #compilerengineer #foundationmodels

Researchers from the University of Pennsylvania have developed a silicon chip that utilizes light waves to accelerate the processing speed and thus reduce energy consumption. The team achieved this by controlling the height of the silicon wafer at a specific region which allowed to control the propagation of light through the chip. The team also ensured the light traveled in a straight line after any scattering thus transferring the information at light speed. The design could also help to reduce the reliance on computational storage capacity (memory) since calculations are done in real-time. The team believes the silicon-photonic (SiPh) chip will find its application in GPU and AI over the coming years #semiconductor #chip #chipdesign #AI #aritificialintelligence Link: https://lnkd.in/g9ghjDtf https://lnkd.in/gKEWtKVH https://lnkd.in/gs8gCR9i Research Paper: https://lnkd.in/g43dNf2h

A possible alternative to the Nvidia H100 GPU’s where demand is well in excess of supply. Perhaps this new innovation in Artificial Intelligence can close the demand gap for processing power.

Artificial intelligence (#AI) is integral to our daily lives, from consumer devices to broader applications such as new drug development, climate change modeling, and #self-driving cars. The high computing power requirements of large AI models are giving rise to rapid iterations of AI chips. "No chip, no AI." The computing power achieved with #AI #chips as a carrier is becoming an important measure of the development level of artificial intelligence. AI chips refer to modules specifically designed to handle a large number of computing tasks in artificial intelligence applications. That is, chips oriented to the field of artificial intelligence are called AI chips. From the perspective of technical architecture, AI chips are mainly divided into four categories: graphics processing unit (#GPU), field programmable gate array (#FPGA), application specific integrated circuit (#ASIC), and brain-inspired chips. Among them, GPU is a general artificial intelligence chip, FPGA and ASIC are semi-customized and fully customized chips based on the characteristics of AI needs, and brain-like chips are processors that imitate the structure and function of the human brain nervous system. One of the advantages of AI is that it can quickly draw feasible solutions from large amounts of data, which can also be used to improve the efficiency of chip design. How is AI used in chip design? 1. Mapping Voltage Drop 2. Predicting Parasitics 3.Place and Routing Challenges 4. Automating Standard Cell Migration The chip industry has developed to a stage where AI assists in the design of AI chips. Chips are becoming larger and larger, especially chips for emerging applications such as AI and high-performance computing, as well as the ultra-large-scale data centers that host these applications. #ai #chips #electronics #electrical #components #emsmanufacturer #odm #oemfactory #pcbassembly #pcbmanufacturer #automotive #industrial #medicaldevices

🚀 Advancing Innovation: The Combination of AI and Semiconductor Technology 🤖 Advances in semiconductor technology and artificial intelligence (#AI) are working together to revolutionize several industries. Now let's explore how these developments are changing the face of technology! 🔍 Performance Enhancement: Reducing the size of nodes to 7nm or 5nm enables the production of quicker and more power-efficient processors, which are essential for speeding up artificial intelligence algorithms. #Technologies #Semiconductors 💡 Greater Computational Power: Powerful CPUs, GPUs, and specialized AI accelerators like TPUs are made possible by decreasing transistor sizes and increasing density. This hardware power is essential for developing and implementing sophisticated AI models. Computational Power #AI Hardware 🛠️ AI-Specific Hardware: State-of-the-art architectures such as GPUs, TPUs, and customized ASICs utilize the most recent advancements in semiconductor technologies to provide high-performance computing specifically designed for AI workloads. #DeepLearning #AIHardware 🔋 Efficiency Gains: In the context of edge devices and the Internet of Things, where power limits are a major concern, smaller nodes equate to reduced power consumption per transistor. AI deployment across a range of devices is made possible by increased efficiency without sacrificing performance. #IoT #EdgeComputing 🧠 Complex Neural Network Structures: The progress in semiconductor technology enables the creation of intricate neural networks, which in turn drive innovations in image identification, natural language processing, and self-governing systems. Neural Networks and Deep Learning 👩💼 AI-Driven Semiconductor Design: By optimizing chip layouts, machine learning algorithms boost performance, cut down on design cycles, and hasten the advancement of semiconductor innovation. Automation in Design and Machine Learning 🏭 Artificial Intelligence for Semiconductor Manufacturing: AI methods improve yield optimization, quality control, defect identification, and predictive maintenance in semiconductor manufacturing, increasing productivity and dependability. #Production #Quality Assurance Are you excited by the combination of AI and semiconductor technology? Take part in the LinkedIn discussion to learn more about the ways in which these breakthroughs are transforming our future! #LinkedInTalk #TechInnovation

Artificial intelligence (#AI) is integral to our daily lives, from consumer devices to broader applications such as new drug development, climate change modeling, and #self-driving cars. The high computing power requirements of large AI models are giving rise to rapid iterations of AI chips. "No chip, no AI." The computing power achieved with #AI #chips as a carrier is becoming an important measure of the development level of artificial intelligence. AI chips refer to modules specifically designed to handle a large number of computing tasks in artificial intelligence applications. That is, chips oriented to the field of artificial intelligence are called AI chips. From the perspective of technical architecture, AI chips are mainly divided into four categories: graphics processing unit ( #GPU), field programmable gate array (#FPGA), application specific integrated circuit (#ASIC), and brain-inspired chips. Among them, GPU is a general artificial intelligence chip, FPGA and ASIC are semi-customized and fully customized chips based on the characteristics of AI needs, and brain-like chips are processors that imitate the structure and function of the human brain nervous system. One of the advantages of AI is that it can quickly draw feasible solutions from large amounts of data, which can also be used to improve the efficiency of chip design. How is AI used in chip design? 1. Mapping Voltage Drop 2. Predicting Parasitics 3. Place and Routing Challenges 4. Automating Standard Cell Migration The chip industry has developed to a stage where AI assists in the design of AI chips. Chips are becoming larger and larger, especially chips for emerging applications such as AI and high-performance computing, as well as the ultra-large-scale data centers that host these applications. #ai #chips #electronics #electrical #components #emsmanufacturer #odm #oemfactory #pcbassembly #pcbmanufacturer #automotive #industrial #medicaldevices

𝐀𝐈 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 𝟐𝟎𝟐𝟒-𝟐𝟎𝟑𝟐. 𝐆𝐥𝐨𝐛𝐚𝐥 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐑𝐞𝐩𝐨𝐫𝐭 𝐓𝐨 𝐊𝐧𝐨𝐰 𝐭𝐡𝐞 𝐆𝐥𝐨𝐛𝐚𝐥 𝐒𝐜𝐨𝐩𝐞 𝐚𝐧𝐝 𝐃𝐞𝐦𝐚𝐧𝐝 𝐨𝐟 𝐀𝐈 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞 𝐌𝐚𝐫𝐤𝐞𝐭. 𝐑𝐞𝐪𝐮𝐞𝐬𝐭 𝐟𝐨𝐫 𝐒𝐚𝐦𝐩𝐥𝐞 𝐏𝐃𝐅: https://lnkd.in/dBAD83_P The #AI hardware market is witnessing robust growth, driven by the increasing demand for efficient and high-performance computing solutions. As AI applications become more sophisticated, there's a growing need for specialized hardware to handle complex computations. It is a class of microprocessors, or microchips, designed to enable faster processing of AI applications, especially in #machine learning, neural networks and computer vision. They are usually designed as manycore and focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. 🌐 𝐊𝐞𝐲 𝐓𝐫𝐞𝐧𝐝𝐬: Accelerated Processing Units (APUs): APUs are gaining traction, integrating both traditional CPUs and specialized AI accelerators on a single chip. This fusion enhances efficiency and accelerates AI workloads. Edge AI Devices: The rise of edge computing is reshaping the AI hardware landscape. Compact, energy-efficient devices are becoming essential for processing AI tasks locally, reducing latency and enhancing privacy. Quantum Computing: Quantum processors are on the horizon, promising unprecedented computational power. While in the early stages, quantum computing holds immense potential for tackling complex AI problems. *𝗕𝘆 𝗧𝘆𝗽𝗲: AIChipsets, AIServers, AIWorkstations *𝗕𝘆 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: BFSI,IT&Telecom,Retail,Manufacturing,PublicSector,Energy&Utility,Healthcare *𝗕𝘆 𝗥𝗲𝗴𝗶𝗼𝗻𝘀: North America, Europe, Asia-Pacific, South America, Middle East & Africa *𝗕𝘆 𝗞𝗲𝘆 𝗣𝗹𝗮𝘆𝗲𝗿𝘀: Graphcore, Intel AI, NVIDIA, Xilinx, Samsung Electronics, Micron Technology, Arm, Google, Adapteva, IBM, Broadberry Data Systems, Huawei, Inspur Systems, Oracle, Ant Group #AIHardware #ArtificialIntelligence #TechInnovation #HardwareRevolution #TechTrends #EdgeComputing #QuantumComputing #APU #NeuromorphicChips #GPU #Innovation #FutureTech #MachineLearning #DeepLearning #TechIndustry #AIInnovation #Collaboration #IndustryInsights #MarketTrends #DigitalTransformation #ComputingSolutions #SmartTechnology #EmergingTech #TechNews

Total worldwide #artificialintelligence (#AI) #semiconductor revenue is expected to reach $71 billion in 2024, a 33% increase from 2023, according to #Gartner's latest forecast. Alan Priestley, research vice president at Gartner, said, "Generative artificial intelligence (#generativeAI) is now driving demand for high-performance #AIchips in data centers. The total value of AI gas pedals used in servers, which are used to offload data processing on microprocessors, will reach $21 billion in 2024, rising to $33 billion by 2028." Gartner predicts that AI personal computers (#PCs) will account for 22 percent of total PC shipments by 2024; and that AI PCs will account for 100 percent of enterprise PC purchases by the end of 2026. Neural Processing Units (#NPUs) in AI PCs are able to extend their runtime, reduce operational noise and temperature, and run AI tasks continuously in the background. This brings new possibilities for the use of AI in daily activities. In 2024, AI chip revenue from computing electronics is expected to reach $33.4 billion, or 47% of total AI #semiconductor revenue; AI chip revenue from #automotiveelectronics and #consumerelectronics is expected to reach $7.1 billion and $1.8 billion, respectively. While most eyes are on the use of high-performance graphics processing units (#GPUs) for new AI workloads, major hyperscale vendors (#AWS, #Google, #Meta, and #Microsoft) are already investing in developing their own AI-optimized chips. #electronics #ElectronicComponents #ArtificialIntelligenceChips #IntelligentChips #EdgeComputingChips