AI Chips Companies

Horizon Robotics

Beijing, China

Horizon Robotics is a China-based AI hardware company specializing in edge AI processing units. They design and manufacture automotive-grade AI chips – the Journey series – specifically for advanced driver-assistance systems (ADAS) and autonomous driving capabilities. Targeting automotive OEMs and Tier 1 suppliers, Horizon Robotics provides a full-stack solution enabling on-device AI processing for improved safety and efficiency in vehicles.

Black Sesame Technologies is a Chinese fabless semiconductor company specializing in high-performance, low-power AI chips for automotive applications. Their core product is the Huashan series of Systems-on-Chips (SoCs), which leverage advanced image processing and adaptive light control sensing technology to enhance autonomous driving capabilities. Black Sesame targets automotive OEMs and Tier 1 suppliers requiring customized and comprehensive AI-powered vision solutions for ADAS and autonomous vehicles.

Cambricon

Beijing, China

Cambricon is a Chinese fabless semiconductor company specializing in the design and development of Neural Processing Units (NPUs) for both cloud and edge computing applications. Their core product is a series of AI chips intended to accelerate machine learning workloads in servers, smart terminals, and robotics. Cambricon targets the growing demand for on-device AI processing and integrated end-to-cloud AI solutions within the Chinese market and beyond.

Rebellions

Seoul, South Korea

Rebellions is a South Korean AI hardware company specializing in high-performance accelerator cards, servers, and rack-scale solutions for data centers and edge computing. Their core product, the REBEL-Quad, utilizes a chiplet architecture and HBM3E memory to deliver industry-leading performance per watt for demanding AI workloads like those built on PyTorch and vLLM. Rebellions targets organizations requiring scalable and efficient AI infrastructure, offering a full-stack hardware and software solution.

Mythic

Austin, United States

Mythic is a US-based AI hardware company developing the Mythic AMP™ analog processor for edge AI applications. Their technology stores AI parameters directly within the processor, eliminating memory bottlenecks and delivering significantly improved power efficiency and performance compared to traditional digital architectures. Mythic targets industries like robotics, defense, and security, enabling advanced, real-time inference at the edge with reduced energy consumption.

d-Matrix

Santa Clara, United States

d-Matrix develops in-memory computing chips—specifically their “Corsair” platform—designed to accelerate Generative AI inference workloads. Their technology integrates memory and compute to deliver ultra-low latency and high throughput, addressing the growing energy consumption and cost challenges of AI deployment. d-Matrix targets enterprises and data centers seeking to efficiently scale Generative AI applications without compromising performance or sustainability.

Untether AI

Toronto, Canada

Untether AI develops specialized hardware accelerating AI inference through an innovative at-memory compute architecture. Their core product, the tsunAImi® accelerator card, significantly improves performance and energy efficiency for demanding AI workloads by integrating computation directly within memory. Targeting applications ranging from assisted driving to smart cities, Untether AI enables cost-effective deployment of AI at the edge and in data centers by supporting standard AI frameworks like TensorFlow and PyTorch.

Axelera AI

Eindhoven, Netherlands

Axelera AI delivers the world's most powerful and advanced solutions for AI at the Edge with the industry-defining Metis AI platform.

DEEPX

Seoul, South Korea

DEEPX develops core technology for high-performance AI semiconductors with over 300 patents pending across the US, China, and Korea.

Kneron

San Diego, United States

Kneron designs and manufactures neural processing units (NPUs) optimized for edge AI applications. Their SoCs enable on-device AI inference, reducing latency and enhancing data privacy compared to cloud-based solutions. Kneron targets smart home, automotive, and industrial IoT markets requiring low-power, high-performance AI capabilities directly within their devices.

Syntiant

Irvine, United States

Syntiant develops neural decision processors that enable on-device AI processing for ultra-low-power applications. Their core technology utilizes at-memory compute to deliver significantly improved efficiency and throughput compared to traditional microcontrollers when running deep learning models. Syntiant targets the mobile, earbud, and IoT markets, providing scalable hardware solutions for applications like voice control, always-on audio recognition, and vibration analysis directly on the edge device.

Sapeon

Seoul, South Korea

Sapeon develops neural processing units (NPUs) specifically designed to accelerate AI inference workloads in data centers. Their technology focuses on delivering high performance and energy efficiency for applications like image recognition and natural language processing. Sapeon targets cloud service providers and enterprises seeking to optimize the cost and performance of their AI-powered services.

AIStorm

San Jose, United States

AIStorm develops charge-domain computing chips and IP that deliver AI, memory, and digital processing directly in silicon—up to 100× more efficient.

Ambient Scientific

Palo Alto, United States

Ambient Scientific pioneers AI-native compute architecture fusing analog efficiency with digital scalability for AI performance.

Blumind

Toronto, Canada

Blumind makes AI accessible and sustainable with innovative analog AI chips for Edge AI applications in AIoT, robotics, and smart mobility.

Supermicro

San Jose, United States

Supermicro designs and manufactures server and storage hardware specifically optimized for demanding artificial intelligence and GPU-accelerated workloads. Their product line focuses on high-performance, modular systems engineered to maximize GPU density and efficiency within data center environments. This positions Supermicro as a key infrastructure provider for organizations deploying AI applications in areas like machine learning, deep learning, and high-performance computing.

Synopsys

Sunnyvale, United States

Synopsys develops Electronic Design Automation (EDA) tools and Semiconductor IP used in the creation of complex integrated circuits. Their solutions increasingly leverage AI, particularly through collaborations with companies like NVIDIA, to accelerate chip design, verification, and heterogeneous integration – crucial for next-generation AI hardware. Synopsys targets companies developing advanced semiconductors for diverse markets including automotive, high-performance computing, and mobile applications, enabling faster time-to-market and improved silicon success rates.

Wolf Speed

Durham, United States

Wolfspeed is a U.S.-based semiconductor company specializing in silicon carbide (SiC) power devices and materials. They manufacture SiC MOSFETs and modules, including 200mm wafers, designed to improve the efficiency and performance of power electronics systems. Wolfspeed targets industries requiring high-performance power solutions, such as electric vehicles, renewable energy, and industrial power supplies, with a focus on enabling scalable and durable designs.

Habana Labs

Caesarea, Israel

Habana Labs, an Intel company, designs high-performance AI processors specifically for deep learning workloads. Their primary product is the Gaudi® series of accelerators, engineered to efficiently handle both training and inference for large-scale generative AI models. Habana Labs targets data center and cloud service providers requiring optimized compute for demanding AI applications like autonomous vehicles and advanced AI deployments.

Qualcomm AI

San Diego, United States

Qualcomm AI develops specialized semiconductor solutions that integrate AI processing directly onto mobile devices and edge computing infrastructure. Their core technology centers on AI engines embedded within Snapdragon platforms and dedicated edge AI compute, enabling on-device machine learning capabilities. This positions Qualcomm as a key enabler for applications requiring low-latency, power-efficient AI processing in areas like smartphones, automotive, and IoT devices.

BrainChip

Sydney, Australia

BrainChip develops and manufactures Akida, a neuromorphic computing processor IP designed for edge AI applications. This technology mimics the human brain to achieve ultra-low power consumption and efficient on-device processing of sensor data, eliminating reliance on cloud connectivity. BrainChip targets developers of edge AI solutions in markets like automotive, industrial automation, and smart devices where low latency and energy efficiency are critical.

Rain AI

San Francisco, United States

Rain AI develops novel AI hardware, specifically neuromorphic computing chips, designed to significantly reduce energy consumption for AI workloads. Their technology focuses on event-driven processing, mimicking biological neural networks to achieve greater efficiency than traditional architectures. The company targets data center operators and AI infrastructure providers seeking to lower operational costs and improve sustainability.