Top 50 Analog Neural Network Chip in Asia

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.

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.

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.

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.

Aantaric Technologies specializes in custom integrated circuit (IC) development, focusing on analog and mixed-signal designs for applications such as power management and automotive controllers. The startup addresses the need for efficient product development in the semiconductor industry by providing end-to-end solutions that accelerate electronic product design and enhance system performance.

Analog provides edge computing solutions that utilize smart sensors and mixed reality devices to enhance real-time data interaction in various environments. This technology enables organizations to better understand and engage with their surroundings, improving operational efficiency and decision-making.

Cortica provides an autonomous AI platform that converts visual, audio, radar and time‑series sensor streams into compressed neural signatures using self‑learning, brain‑inspired networks. The system trains on unlabelled production data, runs inference on low‑power hardware, and adapts continuously to avoid bias, allowing partners in manufacturing, automotive, security, and healthcare to deploy domain‑specific perception and analytics without building foundational models.

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.

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.

RAAAM develops GCRAM, a high-density on-chip memory technology that integrates seamlessly with standard CMOS processes, offering up to 50% area reduction and 10 times lower power consumption compared to traditional SRAM. This technology addresses the limitations of SRAM scaling, enabling semiconductor companies to enhance memory capacity and efficiency in applications such as AI, automotive, and 5G without incurring additional fabrication costs.

BrainQ develops a platform that utilizes artificial intelligence and low-intensity electromagnetic stimulation to enhance neural network activity, facilitating recovery for stroke survivors. The technology aims to reverse long-term disabilities resulting from strokes, enabling patients to regain independence in daily activities and improve their quality of life.

Develops autonomous off-road robots equipped with AI-driven robotic vision and neural network-based control systems, enabling rapid adaptation to real-world environments. These robots are designed for industrial and agricultural applications, improving efficiency and reducing operational challenges in outdoor settings.

The startup develops an AI-based NeuroMosAIc Processor (NMP) that integrates a RISC-V architecture for high-performance computing in semiconductor applications. Its technology enables clients to efficiently evaluate neural network performance metrics such as accuracy, memory bandwidth, and run-time using SDK solutions compatible with TensorFlow, Caffe, PyTorch, and ONNX frameworks.

The startup designs analog and power semiconductor system-on-chip (SoC) solutions that integrate power semiconductor technology with digital signal processing to enhance energy efficiency. This technology enables industrial, telecom, automotive, and consumer applications to minimize energy consumption and promote compact, environmentally friendly systems.

The startup develops a radar signal processing engine that utilizes neural networks and deep learning to enhance radar sensitivity, range, detection, and tracking capabilities. This technology enables ground, naval, and air defense systems to significantly outperform traditional radar algorithms, improving situational awareness and operational effectiveness.

Westwell Lab specializes in neuromorphic circuits and systems, providing advanced solutions for efficient data processing and real-time decision-making in complex environments. The lab addresses the need for improved computational efficiency and adaptability in applications such as autonomous driving and smart logistics.

Cambricon designs and develops artificial intelligence (AI) processors and acceleration cards for cloud, edge, and terminal applications. Their products, including MLUs and IP cores, are built on advanced architectures to enhance AI computing performance. The company also provides software development platforms and systems to support AI deployment.

Axera develops high-performance AI System-on-Chips (SoCs) that utilize hybrid precision processing and pixel-level AI imaging technology to enhance edge computing applications in smart IoT, autonomous driving, and robotics. Their solutions address the need for efficient, high-quality data processing and imaging in complex environments, enabling advanced functionalities in various edge devices.

The startup operates an AI-based neural network platform that utilizes deep nanotechnology to digitize human cognitive abilities, making them accessible for data analysis. This technology enables clients to extract human-level insights from large datasets, enhancing operational efficiency and automating complex tasks.

CanSemi provides customized foundry services for analog integrated circuits on a 12-inch wafer fabrication line. The company offers specialized process technologies to meet the demand for analog chips in automotive, IoT, and 5G applications.

Bioniks manufactures AI‑driven prosthetic arms that use surface EMG sensors and on‑device neural‑network inference to translate user intent into precise movements. The carbon‑fiber, sub‑500 g devices feature modular end‑effectors, BLE connectivity for updates and data streaming, and meet ISO 13485 medical standards for clinical prescription. The system adapts to individual muscle‑signal profiles and provides clinicians with cloud‑synced usage metrics for rehabilitation monitoring.

The startup develops a family of processors optimized for cloud and edge computing, specifically targeting artificial intelligence and machine learning workloads. Their patent-pending chip design technology reduces transistor count while enhancing performance, enabling businesses to lower power consumption and improve yield and reliability.

3PEAK designs and supplies a broad portfolio of high-performance analog integrated circuits, including amplifiers, data converters, and power management solutions. They provide application-specific components for automotive, communication, and industrial electronics, simplifying the selection process for engineers.

The startup offers a semiconductor design platform that utilizes machine learning for automated modeling, enabling self-learning from collected data. This technology reduces development time and minimizes design errors, enhancing the efficiency of semiconductor design processes.

Mobilint develops neural processing unit (NPU) solutions optimized for edge AI applications, achieving up to 80 TOPS performance with low power consumption. Their technology supports over 100 AI algorithm models and provides a user-friendly SDK, enabling efficient development for various edge devices.

Develops neurotechnology and neuromorphic computing solutions that leverage brain-inspired algorithms to process and interpret neural data. This approach addresses challenges in understanding brain function and developing advanced neuro-robotic systems for applications in healthcare, robotics, and artificial intelligence.

Moffett AI designs AI chips that accelerate processing in both terminal and cloud environments, enhancing computational efficiency for AI applications. Their technology addresses the demand for faster and more efficient AI processing capabilities in various industries.

Nemesis develops small, low-power bio-diagnostic solutions utilizing system-on-chip (SoC) technology and intelligent biosignal processing to enhance the accuracy of medical diagnostics. Their technology addresses the need for efficient and precise monitoring of biological signals in medical devices and services.

SoyNet provides an inference-only acceleration solution that enhances the speed of AI model execution through optimized hardware utilization. This technology addresses the latency issues faced by applications requiring real-time AI decision-making, enabling faster and more efficient processing.

Cortical Labs develops biological computing systems that integrate lab-grown human neurons with silicon chips to create hybrid computing platforms. This technology enables more efficient data processing and complex problem-solving capabilities, addressing limitations in traditional computing architectures.

Singularity Dynamics develops machine learning-based verification tools for silicon chip designs, significantly enhancing productivity in the validation process. This technology reduces costs and accelerates time-to-market for custom System-on-Chip (SoC) and CPU designs, addressing the challenges of lengthy verification schedules in the semiconductor industry.

This company develops in-memory computing chips that enable efficient deep learning operations for AIoT applications like wearables and smart devices. Their chips offer high performance at microwatt to milliwatt power levels, reducing computing costs for enterprises.

Chips&Media provides hardware IP solutions for video encoding, decoding, and neural processing units (NPUs). Their IPs enable high-performance, power-efficient video processing and AI acceleration for edge devices, supporting advanced codecs like AV1 and HEVC, and optimized for image processing applications.

The startup is developing augmented neural self-computing technology that enhances data processing efficiency through advanced neural network architectures. This technology addresses the limitations of traditional computing methods by enabling real-time, on-device processing for applications requiring high-speed data analysis.

Listenai designs AI System-on-Chip (SoC) solutions that integrate a tri-core heterogeneous processor with advanced audio and visual interfaces, enabling efficient processing for applications like voice recognition and noise reduction. Their technology addresses the need for high-performance, low-power computing in smart home devices, educational electronics, and consumer products.

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.

AIRIS Labs offers high-performance AI IP cores for ASIC and FPGA integration, enabling efficient on-device machine learning. Their solutions include programmable KNN IP for custom silicon designs and edge AI devices for local facial recognition and temperature monitoring.

The company designs and manufactures high‑performance analog and mixed‑signal integrated circuits for power management, signal conditioning, and data conversion. Its products deliver efficient, reliable operation in demanding industrial and consumer electronic systems.

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.

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.

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.

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.

Ruitai Microelectronics designs and manufactures high-performance analog and analog-digital hybrid chips specifically for intelligent networked vehicles. These chips enhance vehicle communication and control systems, addressing the need for reliable and efficient data processing in modern automotive applications.

SimChip specializes in the design and production of high-end analog integrated circuits, focusing on precision and performance for various applications. The company addresses the need for reliable and efficient analog solutions in industries such as telecommunications and automotive, enhancing signal integrity and reducing power consumption.

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.

Legendsemi designs and develops high-performance analog and mixed-signal chips for applications in telecommunications and consumer electronics. Their technology enhances signal integrity and reduces power consumption, addressing the demand for efficient and reliable electronic components.

C2i Semiconductors specializes in the design of analogue and mixed-signal integrated circuits, addressing the need for high-performance signal processing in various electronic applications. Their solutions enhance device functionality while reducing power consumption and manufacturing costs for clients in the semiconductor industry.

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.

NextVPU designs and manufactures embedded AI visual processing chips, such as the NE-D163A, which enable high-resolution depth data capture and processing for smart devices without the need for external processors. Their technology addresses the challenges of low accuracy and high latency in depth perception for applications in security, robotics, and consumer electronics.

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.