Top 50 Ai Accelerator Chip in Asia

The startup develops a low-power system on a chip (SoC) with an integrated AI accelerator, designed for use in smartwatches, electronic shelf labels, and battery-powered IoT devices. This technology enables manufacturers to create energy-efficient smart devices that enhance user experience and functionality.

Rebellions develops AI accelerators that utilize HBM3e chiplet architecture and 5nm System-on-Chip technology to enhance energy efficiency and computational performance for deep learning applications. The company addresses the need for scalable and efficient AI inference solutions in the rapidly growing generative AI market.

HyperAccel engineers specialized AI semiconductors, utilizing a novel LPU architecture designed for high-performance and energy-efficient Generative AI workloads. Their product line spans from edge devices to cloud datacenters, offering optimized solutions for LLM inference. The company supports leading AI frameworks through a dedicated software platform, ensuring seamless integration for developers.

NEUCHIPS develops AI ASIC solutions, including the Evo Gen 5 PCIe Card and Gen AI N3000 Accelerator, specifically designed for deep learning inference in data centers. Their technology addresses the need for energy-efficient hardware that minimizes total cost of ownership (TCO) while enhancing performance for machine learning applications.

The startup manufactures a chip that utilizes Compute Express Link technology to enable data center operators to efficiently pool and manage artificial intelligence accelerators, processors, and memory. This approach enhances system performance by providing adequate memory resources for diverse device integration, addressing the challenges of scalability and resource allocation in large-scale computing environments.

Hailo develops AI processors optimized for deep learning applications on edge devices, enabling high-performance video processing and analytics with low power consumption. Their technology addresses the need for efficient AI inferencing in various industries, including automotive and industrial automation, by facilitating the deployment of complex neural networks in resource-constrained environments.

Develops on-device AI semiconductor solutions, including custom NPUs, SoC ASICs, and specialized modules, optimized for low power consumption and high performance in applications like video analytics, security, and robotics. By enabling real-time AI processing with support for multiple models on a single chip, DEEPX addresses the challenges of latency, privacy, and network costs associated with cloud-based systems. Its scalable architecture and 259 patents ensure cost-competitive, silicon-proven products for global markets.

EdgeCortix develops the SAKURA-II Edge AI Platform, an energy-efficient AI accelerator that delivers up to 240 TOPS for real-time inferencing in compact, low-power modules. This technology addresses the need for high-performance AI processing at the edge, significantly reducing operational costs across various sectors, including defense, robotics, and smart manufacturing.

NextSilicon's Maverick-2 Intelligent Compute Accelerator (ICA) utilizes software-defined hardware to dynamically optimize performance for high-performance computing (HPC) and artificial intelligence (AI) workloads. This technology eliminates the need for extensive code rewrites, significantly reducing development time and enabling faster insights across various applications.

Qingwei Intelligent Technology designs and manufactures AI chips optimized for high-performance computing tasks in artificial intelligence applications. Their technology addresses the demand for efficient processing power in AI systems, enabling faster data analysis and improved machine learning capabilities.

ThinkForce is a Shanghai-based manufacturer of artificial intelligence chips designed to enhance processing efficiency in machine learning applications. Their technology addresses the demand for high-performance computing solutions in industries reliant on real-time data analysis.

Zhonghao Xinying develops specialized AI chips focused on enhancing computational efficiency for artificial intelligence applications. Their technology addresses the high energy consumption and processing limitations faced by current AI systems, enabling faster and more cost-effective AI deployment.

Hanxu Technology develops specialized computing chips, including the SpinPU-M01 FPGA and SpinPU-M02 ASIC, utilizing spintronics to enhance performance in complex optimization problems. Their technology operates at room temperature and offers significant improvements in speed and accuracy over traditional computing methods, addressing the limitations of quantum computing.

FuriosaAI develops the RNGD data center accelerator, utilizing a Tensor Contraction Processor architecture to enhance the efficiency of AI inference with a power profile of just 150W. This technology enables enterprises to deploy large language models and multimodal applications with low latency and high throughput, significantly reducing energy consumption and operational costs in data centers.

The startup provides AI hardware-software solutions that enhance computational efficiency and performance for data-intensive applications. By integrating specialized AI accelerators with optimized software frameworks, the company addresses the high energy consumption and latency issues faced by traditional computing systems.

Exlords develops an AI and machine learning-integrated hardware platform for edge processors, enabling real-time AI inference on user devices through on-device neural processing units. This technology addresses the need for efficient, localized computing solutions that enhance performance and connectivity in heterogeneous computing environments.

Aivatech develops 3D-vision AIoT chips that integrate advanced facial and palm vein recognition technologies, RGB-D depth cameras, and vehicle license plate recognition systems. These solutions enhance security and automation in various applications, addressing the need for efficient and accurate identification in smart environments.

Micro-nano core designs and produces IoT and AIoT system-on-a-chip (SoC) solutions focused on ultra-low power consumption. Their chips integrate sensor acquisition and embedded AI to improve the energy efficiency, precision, and edge inference capabilities of IoT devices.

Vastai Technologies develops high-performance AI chips specifically designed for computer vision and video processing, including the SV100 series for cloud-based inference and the VE series for edge computing. Their technology addresses the need for efficient, low-latency video analysis and processing in applications such as real-time video streaming and industrial automation.

Netrasemi designs and develops system-on-chips (SoCs) with a domain-specific architecture that enables real-time AI processing on edge devices, addressing the limitations of traditional chip architectures for embedded AI in IoT applications. Their SoCs provide high performance and power efficiency, facilitating the deployment of smart sensors and edge servers across various sectors, including surveillance and autonomous vehicles.

Seehi designs and manufactures artificial intelligence chips that enhance processing efficiency for machine learning applications. Their technology addresses the high energy consumption and performance limitations of traditional computing systems, enabling faster and more efficient AI computations.

Eswin Computing designs integrated chips and solutions for display and video processing, AI data handling, and wireless connectivity. Their technology enhances multimedia system performance and improves data transmission efficiency in various applications.

Timesintelli develops specialized System-on-Chip (SoC) solutions using its proprietary Domain-Specific Architecture (DSA) for edge AI processing, enabling efficient voice and visual data handling in various smart devices. Their AT series chips provide tailored support for multimodal human-machine interactions, addressing the need for cost-effective and energy-efficient processing in the AIoT landscape.

The startup specializes in neuromorphic analog signal processing technology to create ultra-low power AI chips that operate at microwatt levels and microsecond latency. This enables industrial electronics manufacturers to implement neural networks directly at the sensor, effectively addressing the challenges of power constraints in tiny AI applications.

Zhihe Computing develops high-performance, scalable AI computing CPUs based on the RISC-V architecture, enabling efficient processing for AI applications. The company addresses the need for advanced computational power in AI scenarios, leveraging a team with extensive industry experience to enhance performance and scalability.

Optical Standard Technology designs and manufactures silicon photonic computing chips and boards that utilize phase-change materials to enhance computational density and precision for AI applications. This technology significantly reduces deployment and operational costs in high-demand scenarios such as autonomous driving and large-scale data processing.

Xinyaotu provides a full-stack integrated single-chip solution that combines intelligent sensors and computing with proprietary AI algorithms. This technology enables efficient data collection and processing for applications requiring real-time insights, enhancing operational performance in various industries.

Zenosic designs and develops high-performance network chips that enhance data transmission efficiency for AI, cloud computing, and IoT applications. The company addresses the need for robust networking solutions by leveraging its extensive R&D capabilities and a skilled team to deliver reliable chip products that support the demands of the intelligent computing era.

Deep Stream Micro designs high-performance GPU chips for applications like graphics rendering, image processing, and artificial intelligence. Their chips offer super-computing features to accelerate demanding computational tasks.

Ingonyama develops hardware accelerators for Zero Knowledge Proofs (ZKPs), utilizing specialized chip design and algorithms to enhance computational efficiency in cryptographic processes. The company addresses performance bottlenecks in ZK technology, enabling faster and more scalable integration across various computing platforms.

SpacemiT develops high-performance RISC-V architecture computing chips that integrate cloud, edge, and device architectures to enhance data processing efficiency. This technology improves data access speed and reliability by mitigating latency and bandwidth limitations in distributed computing environments.

NeuReality designs AI-centric infrastructure that integrates a network addressable processing unit (NAPU) with purpose-built software to streamline AI inference workflows. This solution reduces reliance on traditional CPUs and networking components, addressing the complexity and inefficiencies that hinder AI model deployment and scalability.

Jeejio develops AI-based chips specifically designed for Internet of Things (IoT) devices, enhancing their processing capabilities and energy efficiency. These chips address the limitations of traditional hardware by enabling real-time data processing and decision-making in resource-constrained environments.

InCore designs customizable RISC-V processor cores and automation tools that streamline the development of system-on-chip (SoC) solutions, significantly reducing design time and costs. Their technology addresses the inefficiencies of traditional chip design methods, enabling faster and more flexible silicon development for various applications.

Huixi Intelligent manufactures high-performance, low-power autonomous driving processors that utilize a unique "data closed-loop defined chip" methodology to enhance vehicle perception and decision-making. Their technology provides the necessary processing power for safe navigation in complex environments, enabling automotive companies to achieve cost-effective and scalable autonomous driving solutions.

Chiplego develops embedded high-performance computing systems that enhance mobile digital experiences by providing efficient processing capabilities. The technology addresses the need for robust computing solutions in mobile applications, enabling seamless performance in data-intensive environments.

Exabits specializes in refining raw GPU assets from leading manufacturers to support advanced AI infrastructure. The company provides access to high-demand hardware, including GB200s, H100s, H200s, and RTX5090s. This focus ensures that organizations have the necessary compute power for demanding AI workloads and innovation.

The startup specializes in on-device AI optimization, enabling efficient machine learning model execution across diverse hardware platforms. This technology reduces latency and energy consumption, enhancing performance for applications in resource-constrained environments.

Kuixin Technology provides integrated circuits that address computing power scaling and interconnection challenges. Their products cater to data centers, artificial intelligence, consumer electronics, automotive, and IoT applications.

CogniFiber provides anomaly detection services for industrial IoT, cybersecurity, and fintech using photonics technology, achieving 100 times faster processing at half the cost. This solution enables organizations to identify and respond to irregularities in real-time, enhancing operational efficiency and security.

Hejian provides industrial software solutions tailored for semiconductor chip manufacturers, focusing on optimizing design and production processes. The company addresses inefficiencies in chip development, enabling faster time-to-market and improved yield rates.

Luchen Technology provides a platform for training and deploying large AI models, significantly reducing training and inference costs by up to 90% while enhancing model capacity and speed. Their solutions enable businesses to efficiently build high-quality AI applications with minimal resources, streamlining the development process across various hardware environments.

RAAAM Technologies offers GCRAM, a high‑density embedded memory that serves as a drop‑in replacement for SRAM in semiconductor chips. The technology delivers up to 50% silicon area reduction and up to 10× lower power consumption while remaining compatible with standard CMOS processes. It enables higher on‑chip memory capacity for AI, ML, AR/VR, automotive, 5G, and data‑center applications.

XCENA develops proprietary architecture to address the 'Memory Wall' limitations faced by hyperscale data centers. Their product line includes Computational Memory, Memory Pool solutions, and a specialized HW-SW Co-op Architecture. These offerings aim to accelerate data processing capabilities essential for advancing artificial intelligence technologies.

Rapidus develops and manufactures next-generation logic semiconductors, focusing on 3D LSI and Nano Sheet GAA technologies to enhance domestic semiconductor production capabilities. The company addresses the critical need for economic security in semiconductor supply chains by fostering collaboration with global research institutions and promoting energy-efficient manufacturing practices.

NeuroBlade develops a purpose-built SQL Processing Unit (SPU) architecture that enhances data processing speed and efficiency, achieving up to 100x acceleration for complex queries. This technology addresses the limitations of traditional CPU architectures, enabling organizations to analyze petabyte-scale data more effectively while reducing infrastructure costs and power consumption.

Silicon Box specializes in advanced semiconductor integration, focusing on next-generation chiplet adoption solutions. The company operates a state-of-the-art foundry utilizing sub-5 micron technology for high-density packaging. They provide expertise across multiple sectors to enable scalable deployment of complex chiplet-based platforms.

Super Rui Technology develops high-performance, energy-efficient multi-core processors based on the RISC-V architecture, addressing the need for reliable and secure computing solutions in various applications such as edge computing and autonomous driving. Their custom chip development services cater to specific computational requirements, enhancing performance and efficiency in demanding environments.

OPENEDGES Technology offers integrated memory subsystem IP solutions for ASIC and ASSP development. Their IP suite, including DDR PHY, memory controllers, and interconnects, optimizes power, silicon area, and DRAM performance for advanced computing applications.

ADChips offers integrated SoC design and development services, providing a portfolio of processors and IP cores to accelerate the creation of custom semiconductor solutions. They enable companies to integrate advanced functionality into embedded systems and consumer electronics without extensive in-house ASIC design capabilities.