Latest news with #edgeAI
Yahoo
7 hours ago
- Business
- Yahoo
Ambiq Micro, Inc. Announces Filing of Registration Statement For Proposed Initial Public Offering
AUSTIN, Texas, July 07, 2025 (GLOBE NEWSWIRE) -- Ambiq Micro, Inc. ('Ambiq'), a technology leader in ultra-low-power semiconductor solutions for edge AI, today announced that it has filed a registration statement on Form S-1 with the U.S. Securities and Exchange Commission (the 'SEC') relating to the proposed initial public offering of its common stock. The proposed offering is subject to market and other conditions and there can be no assurance as to whether or when the proposed offering may be completed. The number of shares of common stock to be offered and the price range for the proposed offering have not yet been determined. Ambiq intends to apply to have its common stock listed on the New York Stock Exchange under the symbol 'AMBQ.' BofA Securities and UBS Investment Bank will act as joint lead book-running managers for the proposed offering. Needham & Company and Stifel will act as joint book-running managers for the proposed offering. The proposed offering will be made only by means of a prospectus. When available, copies of the preliminary prospectus relating to the proposed offering may be obtained by contacting: BofA Securities, NC1-022-02-25, 201 North Tryon Street, Charlotte, North Carolina 28255-0001, Attention: Prospectus Department, or by email at or UBS Securities LLC, Attention: Prospectus Department, 1285 Avenue of the Americas, New York, New York 10019, by telephone at (888) 827-7275 or by emailing ol-prospectus-request@ A registration statement relating to these securities has been filed with the SEC but has not yet become effective. These securities may not be sold, nor may offers to buy be accepted, prior to the time the registration statement becomes effective. This press release shall not constitute an offer to sell or the solicitation of an offer to buy these securities, nor shall there be any sale of these securities in any state or jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such state or jurisdiction. About Ambiq Ambiq's mission is to enable intelligence (artificial intelligence (AI) and beyond) everywhere by delivering the lowest power semiconductor solutions. Ambiq enables its customers to deliver AI compute at the edge where power consumption challenges are the most profound. Ambiq's technology innovations, built on the patented and proprietary subthreshold power optimized technology (SPOT®), fundamentally deliver a multi-fold improvement in power consumption over traditional semiconductor designs. Ambiq has powered over 270 million devices to date. Contact: Charlene Wan VP of Corporate Marketing and Investor Relations cwan@ A photo accompanying this announcement is available at in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
3 days ago
- Business
- Yahoo
Edge AI Hardware Market worth $58.90 billion by 2030 - Exclusive Report by MarketsandMarkets™
DELRAY BEACH, Fla., July 4, 2025 /PRNewswire/ -- The edge AI hardware market is projected to reach USD 58.90 billion by 2030 from USD 26.14 billion in 2025, at a compound annual growth rate (CAGR) of 17.6% according to a new report by MarketsandMarkets™. A key market driver for edge AI hardware is the growing deployment of IoT devices across various industries, including smart homes, industrial automation, healthcare, and transportation. Many of these applications require real-time data processing so decision-making can occur locally rather than in the cloud. Also, with IoT device implementations, we are seeing more and more demands for lower latency with enhanced privacy and data security as edge AI allows sensitive data and information to be processed locally on devices rather than sent to another location to be processed externally. Further, improvements in hardware architectures that strengthen energy-efficient designs and provide solutions designed based on specific industries have significantly impacted market growth. The upsurge of new, high-performance processors and new software platforms is altering operational workflows and broadening the scope of AI-based applications at the edge. Download PDF Brochure: Browse in-depth TOC on "Edge AI Hardware Market" 260 – Tables70 – Figures303 – Pages Edge AI Hardware Market Report Scope: Report Coverage Details Market Revenue in 2025 $ 26.14 billion Estimated Value by 2030 $ 58.90 billion Growth Rate Poised to grow at a CAGR of 17.6% Market Size Available for 2021–2030 Forecast Period 2025–2030 Forecast Units Value (USD Million/Billion) Report Coverage Revenue Forecast, Competitive Landscape, Growth Factors, and Trends Segments Covered By device, power consumption, processor, function, vertical, and region Geographies Covered North America, Europe, Asia Pacific, and Rest of World Key Market Challenge Optimizing power consumption in edge AI systems Key Market Opportunities Market Potential for Ultra-Low Latency AI in 5G-Powered Edge Infrastructure Key Market Drivers Rising emphasis on launching innovative AI co-processors for edge AI applications CPU processors to capture the most significant market share throughout the forecast period. CPUs have the largest market share of processors for edge AI hardware. CPUs' versatility, scalability, and general-purpose nature allow them to process any type of AI task in real time as needed by smart factories, autonomous devices, and industrial IoT applications. CPUs will see mass adoption due to the requirement for on-board computing, which can process large data volumes and execute workloads simultaneously. In other words, CPUs are important processing engines for consumer edge AI platforms and crucial processing engines for enterprise applications requiring on-board computing. CPUs are not as specialized as other processors. They can support a heterogeneous environment of diverse edge applications, whether a smartphone, wearable device, or much more advanced industrial systems, or simple two-factor authentication applications. In short, the adaptiveness and ubiquity of CPUs will ensure that CPUs will be the heavyweight champion of processing units for any edge AI deployment (despite many newer types of processors becoming popular for specialized applications, such as GPUs and ASICs). Robots witness the second-highest CAGR during the forecast period. Based on several existing technological changes and industry trends, Robots will record the second-highest CAGR in the edge AI hardware market growth over the forecast period. However, as robotics will increasingly combine artificial intelligence (AI) with edge computing, previous examples provided in this report of "edge AI" hybridity will be more likely to accelerate this convergence away from cloud (and hapless potential for near-real-time decision making). Most robots will leverage the growing capacity to process large local data volumes. This will enable robots to make split-second decisions without the caveat of cloud infrastructure. Timely local choices are crucial for autonomous vehicles, industrial automation, and healthcare robotics applications. In many of these applications, milliseconds matter for safety and efficiency. Edge AI decisions made in real time based on large datasets will allow robots to adapt and learn from their increasingly changing surrounding environments rapidly. Robots will also have a better chance of optimizing the dynamic decision-making process with improved accuracy while running advanced machine learning algorithms on the device. Inquiry Before Buying: Asia Pacific to account for the most significant share of edge AI hardware market. Asia Pacific has the largest share of the edge AI hardware industry, driven by increased adoption of IoT devices and significant investments in AI-driven technologies in many countries (China, Japan, South Korea, and India). Furthermore, the region provides a growing consumer electronics market (smartphone and wearable technology) that requires efficient AI processing, preferably at the edge. Major economies are turning to edge AI applications for smart homes, smart factories, healthcare, and autonomous vehicles. Strong government initiatives and collaborations with leading global technology companies will support the approach. Therefore, with the fastest CAGR and ongoing product and inherent process innovation, the Asia Pacific region is likely to remain ahead in terms of edge AI hardware as it caters to growing industries that value real-time analytics, data privacy, and reduced latency. Major vendors in the edge AI hardware companies include Qualcomm Technologies, Inc. (US), Huawei Technologies Co., Ltd. (China), SAMSUNG (South Korea), Apple Inc. (US), MediaTek Inc. (Taiwan), Intel Corporation (US), NVIDIA Corporation (US), IBM (US), Micron Technology, Inc. (US), and Advanced Micro Devices, Inc. (US). Get 10% Free Customization on this Report: Browse Adjacent Market: Semiconductor and Electronics Market Research Reports &Consulting See More Latest Semiconductor Reports: Embodied AI Market by Product Type [Robots (Humanoid Robots, Mobile Robots, Industrial Robots, Service Robots, Cobots), Exoskeletons, Autonomous Systems, Smart Appliances], Level of Embodiment (Level 1, Level 2, Level 3) - Global Forecast to 2030 AI Data Center Market by Offering (Compute Server (GPU-Based, FPGA-Based, ASIC-based), Storage, Cooling, Power, DCIM), Data Center Type (Hyperscale, Colocation), Application (GenAI, Machine Learning, NLP, Computer Vision) - Global Forecast to 2030 About MarketsandMarkets™ MarketsandMarkets™ has been recognized as one of America's Best Management Consulting Firms by Forbes, as per their recent report. MarketsandMarkets™ is a blue ocean alternative in growth consulting and program management, leveraging a man-machine offering to drive supernormal growth for progressive organizations in the B2B space. With the widest lens on emerging technologies, we are proficient in co-creating supernormal growth for clients across the globe. Today, 80% of Fortune 2000 companies rely on MarketsandMarkets, and 90 of the top 100 companies in each sector trust us to accelerate their revenue growth. With a global clientele of over 13,000 organizations, we help businesses thrive in a disruptive ecosystem. The B2B economy is witnessing the emergence of $25 trillion in new revenue streams that are replacing existing ones within this decade. We work with clients on growth programs, helping them monetize this $25 trillion opportunity through our service lines – TAM Expansion, Go-to-Market (GTM) Strategy to Execution, Market Share Gain, Account Enablement, and Thought Leadership Marketing. Built on the 'GIVE Growth' principle, we collaborate with several Forbes Global 2000 B2B companies to keep them future-ready. Our insights and strategies are powered by industry experts, cutting-edge AI, and our Market Intelligence Cloud, KnowledgeStore™, which integrates research and provides ecosystem-wide visibility into revenue shifts. To find out more, visit or follow us on Twitter , LinkedIn and Facebook . Contact: Mr. Rohan SalgarkarMarketsandMarkets™ INC. 1615 South Congress 103, Delray Beach, FL 33445USA: +1-888-600-6441Email: sales@ Our Web Site: Insight: Source: Logo: View original content: SOURCE MarketsandMarkets Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Forbes
26-06-2025
- Business
- Forbes
Edge AI Applications As The Catalyst For AI PC Market Growth
Ajith Sankaran, Executive Vice President, C5i. getty Despite all the buzz, the adoption of high-performance AI PCs with powerful neural processing units (NPUs) has been especially sluggish. Since their launch in mid-2024, these devices have captured just 5% of AI PC market sales. This can be attributed to several factors: • AI PCs typically command a significant price premium without clearly articulated benefits. Many users remain unconvinced that these costs translate to meaningful improvements in computing experiences. • Compatibility concerns persist, particularly with first-generation advanced RISC machine (ARM)-based systems that may not support legacy software. • There is a scarcity of software applications that fully harness AI PC capabilities. According to a 2024 ICD report, the global market for personal computing devices was "set to grow 3.8% in 2024, reaching 403.5 million units." However, this growth is primarily driven by a nearly double-digit growth in tablets. According to Jitesh Ubrani of IDC, 'There seems to be a big disconnect between supply and demand as PC and platform makers are gearing up for AI PCs and tablets to be the next big thing, but the lack of clear use cases and a bump in average selling prices has buyers questioning the utility.' I believe the answer to realizing the potential of AI PCs in enterprise scenarios lies in understanding and utilizing edge AI. To understand why, let's take a closer look at how these systems operate. Edge AI And Its Relationship With AI PCs Edge AI represents the convergence of AI and edge computing, enabling AI algorithms to run directly on local devices rather than in remote data centers. This approach processes data where it's generated, eliminating the need to send information to the cloud for analysis and returning results almost instantaneously. AI PCs are well-positioned to serve as powerful edge AI platforms due to their unique hardware architecture. They integrate three processing components: • A central processing unit (CPU) for general computing tasks. • A graphics processing unit (GPU) for parallel processing workloads. • A neural processing unit (NPU) optimized for AI computations. This triad of capabilities allows AI PCs to handle edge AI applications with efficiency. The performance benefits can be substantial; security company CrowdStrike reported that its software's CPU consumption dropped from 35% to 1% when running on machines equipped with Intel NPUs. Global shipments of AI PCs are projected to reach 114 million units in 2025, accounting for 43% of all PC shipments. I believe that edge AI that incorporates the latest advances in generative AI and agentic AI could provide tangible benefits that justify the premium pricing of AI PC for consumers and enterprises. As more developers create software that leverages NPUs and other specialized AI hardware, the value proposition should become clearer, driving increased adoption across both consumer and enterprise segments. Emerging Edge AI Applications Driving AI PC Demand • Manufacturing Intelligence Manufacturing environments are proving to be fertile ground for edge AI applications. AI systems running locally on AI PCs can monitor equipment health in real time, detecting anomalies and predicting potential failures before they occur. This can reduce costly downtime. Quality control represents another application. AI-powered cameras connected to edge computing systems can inspect products for defects with precision and consistency. • Healthcare Innovations The healthcare sector also stands to benefit from edge AI. Portable diagnostic devices equipped with edge5 AI can analyze medical images such as X-rays, MRIs, and CT scans locally, providing rapid insights without requiring cloud connectivity. This is particularly valuable in remote areas. And wearable health devices using edge AI can analyze biometric data locally, detect anomalies and alert healthcare providers without transmitting sensitive patient information to remote servers. • Retail Transformation In retail, edge AI applications are revolutionizing operations and customer experiences. AI-powered cameras and sensors can track inventory levels in real time, optimizing stock replenishment. The same infrastructure can analyze customer behavior patterns, enabling retailers to deliver personalized recommendations and promotions. These capabilities require significant local processing power that can be provided by AI PCs to analyze video feeds and sensor data in real time. • Security and Privacy Protection Edge AI can deliver faster performance while keeping sensitive data local instead of sending it to cloud services. For example, Bufferzone NoCloud "uses local NPU resources to analyze websites for phishing scams using computer vision and natural language processing." Edge AI applications can enhance banking security by detecting unusual transactions and immediately alerting users. Recommendations For Effective AI PC and Edge AI Adoption 1. Develop edge-native AI applications for real-time decision-making. Prioritize building edge-native AI applications that leverage the NPUs in your organization's AI PCs to execute machine learning models locally. For example, manufacturing firms can deploy vision systems on AI PCs to perform real-time quality inspections directly on production lines, reducing defect rates while eliminating cloud dependency. 2. Deploy agentic AI systems for autonomous workflow optimization. Agentic AI excel at autonomously managing complex, multi-step processes. In supply chain, running agentic AI systems on AI PCs can allow you to dynamically reroute shipments based on real-time traffic data processed at the edge, reducing delivery delays. Financial institutions can also combine agentic AI with edge computing to autonomously monitor transactions for fraud patterns, triggering immediate alerts while keeping sensitive financial data localized. 3. Implement privacy-centric AI architectures for regulated industries. Consider adopting hybrid edge-cloud AI architectures to balance computational demands with regulatory compliance. For example, banks can deploy on-premise AI PC clusters to run agentic AI fraud detection systems, ensuring customer transaction data never leaves internal networks. 4. Build scalable edge AI infrastructure with modular hardware. Invest in AI-optimized hardware ecosystems that support both current and emerging workloads. For instance, consider deploying AI PCs with dedicated NPUs for employee productivity tools and pairing them with edge servers containing GPU/TPU arrays for heavy computational tasks. 5. Integrate generative AI with edge computing for adaptive systems. By fusing generative AI with edge computing, you can enable dynamic system adaptation within your company. For example, manufacturers can deploy small language models on AI PCs to generate equipment repair instructions tailored to real-time sensor data, reducing machine downtime. Conclusion While initial adoption of AI PCs has been slow due to high costs, compatibility issues and a lack of applications, the emergence of edge AI use cases is beginning to demonstrate the value of local AI processing. As developers increasingly leverage NPUs to build edge-native and agentic AI solutions, I believe the value proposition of AI PCs will become more evident, driving broader adoption across consumer and enterprise markets. 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Forbes
30-05-2025
- Business
- Forbes
Japanese Semiconductor Startup Secures $21 Million In Grants For Edge AI
EdgeCortix's Sakura-II module. As Japan races to develop its domestic semiconductor ecosystem, Tokyo-headquartered chip design startup EdgeCortix announced it received 3 billion yen ($21 million) from a government-backed agency to develop specialized chips that can power 'edge AI,' a rapidly growing field of AI that involves running applications on devices themselves instead of on the cloud. The fresh funds, in the form of a project award from Japan's New Energy and Industrial Technology Development Organization (NEDO), bring the five-year-old company's total funding to $86 million, including $49 million in non-dilutive government grants and $37 million in equity financing. It received a 4 billion yen ($27.7 million) subsidy from a separate NEDO program last November. Across three previous equity funding rounds, with the most recent being a $20 million raise in October 2023, the startup's investors include SBI Investment, a CVC unit of Japanese financial services conglomerate SBI Group; Monozukuri Ventures; Seoul-based VC firm Futureplay; and automotive chips maker Renesas Electronics, formerly under Japanese electronics giant NEC. Renesas is also a customer of EdgeCortix. 'Building systems that are significantly more performance-per-watt efficient for AI processing than the current status quo, whether that's GPUs or other types of systems, especially in constrained environments…that is a critical factor for almost all edge applications,' says Sakyasingha Dasgupta, founder and CEO at EdgeCortix, in a video interview. 'That essentially differentiates us from the broader edge AI market.' In addition to the startup's focus on optimizing energy efficiency, Dasgupta adds, what distinguishes EdgeCortix is its architecture, referring to the design and programming that powers chips. Its patented 'Dynamic Neural Accelerator' architecture is an IP core, akin to a 'brain' for AI computing that can direct processors within a chip and adjust the way its components interact. This IP core can be integrated with processors such as neural processing units (NPU), which are tailored for machine learning. The latest grant will finance the development of EdgeCortix's new chiplet, a type of chip that uses interchangeable components, as opposed to monolithic ones. Dubbed 'NovaEdge,' EdgeCortix's chiplet for edge AI is designed for high-performance generative AI inference and on-device learning, the company says. Founded in 2019, EdgeCortix operates as a fabless semiconductor company, meaning it does not own its own fabrication facility, or 'fab.' The NovaEdge chiplet will utilize a 12-nanometer node produced by billionaire Morris Chang's Taiwan Semiconductor Manufacturing Co. (TSMC). EdgeCortix plans to commence mass production at TSMC subsidiary Japan Advanced Semiconductor Manufacturing (JASM)'s facility in Kumamoto, Japan, by 2027. A plant of Japan Advanced Semiconductor Manufacturing Company (JASM). With a wide range of applications, ranging from robotics to industrial automation, EdgeCortix's chips and accompanying software have recently gained traction in the defense industry. Earlier in May, the startup inked an agreement with the U.S. Department of Defense's venture-oriented Defense Innovation Unit (DIU) to use EdgeCortix's products for defense technologies, including AI-powered vision and generative AI. In December, the DIU had announced it would launch a new effort to accelerate the adoption of generative AI in both warfighting and enterprise management. Specifically, on the battlefield, edge AI may help quickly process sensitive information in environments with limited network connectivity or potential cybersecurity threats. Tech giant Palantir–cofounded by billionaires Peter Thiel, Alexander Karp, Stephen Cohen and Joe Lonsdale–has developed a range of edge AI offerings for military purposes, including Skykit, a backpack-sized server that can act as a fully operational intelligence unit for soldiers, analyzing data from sensors on drones and surveillance equipment. In the nascent field of edge AI, the decentralization of AI represents 'a profound shift in the technological landscape,' according to a report published in February by consulting firm Deloitte. Such computing developments may be particularly effective for use cases 'requiring rapid responses or operating in disconnected environments,' including smart home devices, autonomous vehicles, wearable health monitors, and industrial Internet of Things (IoT) systems. Global spending on edge infrastructure is projected to grow from $25.3 billion in 2022 to $55.6 billion by 2027, the report added, citing research from the International Data Center (IDC). A Palantir Technologies Skykit on display. Japan's government-backed investments align with broader efforts to bolster domestic chip design and manufacturing, with the aim of establishing greater independence for advanced AI technologies. Last November, Prime Minister Shigeru Ishiba announced a $65 billion plan to invest in the country's chip and artificial intelligence industry by 2030, according to local media. Central to these efforts is the state-backed chipmaker Rapidus. In March, the Japanese government pledged an additional $5.4 billion to Rapidus, bringing its total government subsidies or grants to around $11.5 billion. Headquartered in Tokyo and backed by industry giants, including financial services groups MUFG Bank and SoftBank, electronics makers NEC and Sony, Toyota, and telecoms provider NTT, Rapidus aims to launch commercial production of 2-nanometer chips—some of the world's thinnest and most advanced—by 2027. A major link in the global semiconductor supply chain, Japan is also home to industry giants including billionaire Uchiyama family's Lasertec, which manufactures chip testing equipment; KKR-backed chip production equipment maker Kokusai Electric; Bain-backed Kioxia, Advantest (chip testing equipment); and Sumco, a silicon wafer supplier.
Yahoo
27-05-2025
- Business
- Yahoo
Gcore Appoints AI Expert Prof. Dr. Feiyu Xu as Board Advisor
Gcore welcomes Prof. Dr. Feiyu Xu, a leading figure with expertise in AI research and industry LUXEMBOURG, May 27, 2025 /PRNewswire/ -- Gcore, the global edge AI, cloud, network, and security solutions provider, today announced it has strengthened its board with the addition of AI expert, Prof. Dr. Feiyu Xu. A renowned AI researcher, Prof. Dr. Xu has held pivotal leadership roles at SAP, Lenovo, and the German Research Centre for AI. Her appointment reinforces Gcore's commitment to driving AI innovation and industry leadership. Prof. Dr. Xu will help shape the company's AI vision, growth strategies, and long-term goals. Since its founding in 2014, Gcore has continually expanded its product suite, most recently with Everywhere Inference, which offers flexible deployment options, including cloud, on-premise and hybrid environments. By running on edge points of presence, Everywhere Inference enables faster processing and response time for end user and applications. As the first Gcore Board Advisor to hold a leading position in AI research and innovation, Dr. Xu will guide Gcore's growth and innovation in the edge AI field. Today, Gcore provides global infrastructure across six continents that enables businesses to train and deploy AI models globally with ultra-low latency. With 180+ edge locations, including more than 50 cloud locations and 14,000 peering partners, Gcore brings AI workloads closer to users for faster, more scalable, and cost-efficient performance. As the company continues to grow, Prof. Xu's expertise will consolidate Gcore's position as a world-leading innovator in AI. Prof. Xu said: "I am thrilled to be joining as Gcore's Board Advisor, a unique tech company with immense potential. Gcore not only provides the infrastructure needed for developing AI applications, but it also actively contributes to the growth of the AI ecosystem in Europe. I am delighted to be part of a team that will shape the future of technology and innovation." Andre Reitenbach, CEO of Gcore, commented: "It is an honor to welcome Prof. Xu as Board Advisor. Prof. Xu's strategic vision and deep expertise in AI will make her an exceptional addition to the board. As a renowned scientist and business leader with a deep understanding of AI, Prof. Xu's insights will play an integral role in advancing Gcore's mission to connect the world to AI anywhere and anytime." About Gcore Gcore is a global edge AI, cloud, network, and security solutions provider. Headquartered in Luxembourg, with a staff of 600+ operating from ten offices worldwide, Gcore provides its solutions to global leaders in numerous industries. The company manages its own global IT infrastructure across six continents, with one of the best network performances in Europe, Africa, and LATAM, due to the average response time of 30 ms worldwide. Gcore's network consists of 180+ points of presence around the world in reliable Tier IV and Tier III data centres, with a total capacity exceeding 200 Tbps. Learn more at and follow them on LinkedIn, Twitter, and Facebook. Photo - View original content to download multimedia: SOURCE Gcore Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
