Top 50 Analog Neural Network Chip

The startup develops a neuromorphic chip that mimics human brain information-processing mechanisms to enhance artificial intelligence hardware. This technology addresses computing bottlenecks by enabling more efficient training of neural networks for AI applications.

Innatera provides ultra-low-power neuromorphic processors for edge AI applications. Their spiking neural processors enable real-time pattern recognition with sub-milliwatt power consumption and significantly reduced latency for battery-powered devices.

Provides scalable neural processor unit (NPU) semiconductor IP with a packet-based architecture that enables parallel execution of AI workloads, achieving up to 90% processor utilization. This approach reduces memory overhead, power consumption, and latency while supporting complex AI models across edge devices in industries like mobile, automotive, and industrial automation.

SynSense develops mixed-signal neuromorphic processors that achieve ultra-low power consumption and low-latency performance for edge computing applications. Their technology addresses the challenges of high energy use and slow response times in AI systems, enabling efficient real-time processing across various domains such as robotics, smart homes, and autonomous driving.

Mach42 utilizes proprietary neural network technology to accelerate the verification process of analog circuit designs, achieving high accuracy with minimal data input. This platform significantly reduces design cycle times, enabling faster time-to-market for complex simulations in engineering and scientific applications.

Agile Analog provides customizable, multi-process analog IP technology that enables semiconductor designers to create optimized, fab-ready analog components tailored to specific applications and foundry processes. This approach reduces the complexity and cost associated with traditional analog IP integration, allowing for greater control over the design flow and faster development cycles.

SpiNNcloud Systems develops specialized hardware that replicates brain-like parallel processing to enhance real-time computing for complex simulations and data analysis. This technology overcomes the limitations of traditional computing architectures, significantly improving efficiency in handling large-scale data tasks.

Ambient Scientific develops the GPX10, an ultra-low power AI microprocessor designed for on-device applications, enabling years of operation on a single coin cell battery. This technology addresses the limitations of cloud dependency by providing efficient, always-on AI capabilities for battery-powered edge devices, enhancing data privacy and reducing operational costs.

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.

Aspinity develops an analog machine learning processor that enhances battery-operated, always-on sensing devices by improving energy efficiency and extending battery life by ten times. This technology enables precise event detection and classification in applications such as IoT, smart home, and wearable health monitoring, while minimizing power consumption from irrelevant data processing.

EnCharge AI develops a scalable analog in-memory computing platform that enhances AI performance by achieving 20 times higher efficiency and 10 times lower total cost of ownership compared to traditional GPU solutions. This technology enables on-device processing, significantly reducing CO2 emissions and ensuring data privacy while making advanced AI accessible beyond cloud infrastructure.

The startup develops AI processors that mimic human brain architecture to enhance energy efficiency and reduce complexity in AI systems. By addressing the limitations of current chip technology, their solutions enable clients to achieve high performance with minimal power consumption.

Kneron develops application-specific integrated circuits (ASICs) and software that provide artificial intelligence tools for edge computing. Their technology enhances processing efficiency and reduces latency for AI applications in resource-constrained environments.

Salience Labs is developing a hybrid photonic-electronic chip designed to enhance the processing speed and energy efficiency of artificial intelligence applications. This technology addresses the limitations of traditional electronic chips by enabling faster data transfer and lower power consumption, crucial for scaling AI systems.

Syntiant develops Neural Decision Processors™ that enable the deployment of deep learning models on power-constrained edge devices, significantly enhancing efficiency and throughput compared to traditional microcontrollers. Their technology addresses the limitations of cloud dependency by providing ultra-low-power, high-performance processing for applications in battery-powered products like hearing aids and smart speakers.

The startup develops a deep-tech semiconductor chipset that enhances data movement by bringing data processing closer to computation. This technology improves the execution speed of large language models while increasing data privacy and energy efficiency.

The startup develops soft implantable electrodes that record and electrically stimulate neural tissues to diagnose and treat neurological diseases. Their compliant neural interfaces achieve long-term bio-integration, conforming to the mechanics of neural tissue, which enhances therapeutic outcomes for patients.

Neurophos develops a photonic computing architecture that utilizes ultra-dense optical modulators to achieve 160,000 TOPS at 300 TOPS per watt, significantly outperforming traditional GPUs. This technology addresses the escalating demand for AI compute power by providing a solution that replaces 100 GPUs with a single processor while consuming only 1% of the energy.

The startup develops neuro-sensing technology that facilitates direct communication between the human brain and artificial intelligence systems. This platform enhances user interaction with machines while improving the understanding of neurological disorders, enabling more intuitive and effective human-computer interfaces.

The startup develops ultra-low power AI solutions for edge devices, enabling real-time data processing without reliance on cloud infrastructure. This technology addresses the need for energy-efficient smart devices in environments with limited power availability.

MintNeuro develops scalable semiconductor technology for next-generation neural implants that enhance the treatment of neurological conditions through compact, low-power solutions. The company aims to improve patient outcomes by enabling minimally invasive procedures that offer high performance and accessibility in medical interventions.

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.

Twistient is developing neuromorphic processors that utilize novel transistors for low-power, compute-in-memory designs, mimicking human brain processing at room temperature. This technology addresses the inefficiencies of traditional von Neumann architectures, enabling ultra-low-power edge AI applications while significantly reducing energy consumption.

Neucom provides the ADA platform, a neuromorphic processing system that enables low-power, event-based computation for edge devices. Its Turing-complete architecture and user-friendly SDK allow developers to adapt complex algorithms, including post-quantum cryptography, for efficient implementation without prior spiking neural network expertise.

ANAFLASH develops energy-efficient neuromorphic processors that enable real-time on-device AI processing for smart edge devices. Their technology reduces data movement and enhances computational efficiency, addressing the limitations of external data transfer in battery-powered applications.

Aeonsemi develops analog mixed-signal DSP-centric integrated circuits, including the ChronoPHY™ multi-rate 10G Ethernet PHYs and Nemo™ multi-Gigabit Ethernet chipset, to enhance network communication performance. Their products provide low latency and high power efficiency, addressing the demands for reliable bandwidth and synchronization in modern communication systems.

The startup develops neuromorphic algorithms and low-energy machine vision systems that utilize asynchronous event-based processing to enhance predictive decision-making. This technology enables clients to minimize reliance on large datasets, significantly lowering costs associated with AI deployment.

Orca Semiconductor designs and manufactures custom semiconductor solutions, optimizing for size, efficiency, and connectivity. They partner with clients to create specialized chips that improve product performance and reduce environmental impact.

The startup develops AI-driven software that automates the design and training of neural networks for artificial vision applications. This platform enables companies to deploy optimized vision models that achieve high accuracy while minimizing power consumption.

Blumind develops analog machine learning inferencing engines tailored for edge smart sensors and devices, enhancing real-time data processing in resource-constrained environments. This technology enables efficient decision-making by allowing devices to analyze data locally without relying on cloud computing.

Sphere Semi uses a proprietary AI engine to autonomously generate, evaluate, and optimize analog circuit layouts for custom RF components and mixed-signal IP. This accelerates the delivery of production-ready chips and reusable IP blocks, surpassing human design baselines and reducing development timelines.

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.

The startup develops an artificial neural network technology to optimize proprietary trading in short-cycle physical markets, specifically targeting international power markets. By enhancing energy production balancing, the company helps consumers transition to low-carbon energy solutions while reducing operational costs.

Neurobus develops neuromorphic computing solutions that enable real-time data processing for autonomous systems in aerospace and defense, utilizing bio-inspired technologies to enhance power efficiency and performance. Their technology addresses the challenges of low power consumption and high computational demands in resource-constrained environments, particularly for space navigation and situational awareness.

Nanochap develops advanced neural interface technology and programmable biochips for precise neural stimulation and biosensing applications. Their solutions address the need for accurate health monitoring and treatment in medical and wellness sectors, enhancing patient outcomes through real-time data collection and analysis.

anabrid develops LUCIDAC, a fully reconfigurable analog computer that operates alongside digital systems to process complex mathematical problems with high speed and energy efficiency. This hybrid computing technology directly handles analog data, significantly reducing energy consumption while enabling real-time applications in AI acceleration and climate modeling.

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.

The startup develops quantum computing technology that integrates memory and processing components to enhance computational efficiency. Its proprietary self-organizing logic gates reduce the processing time for complex optimization problems from hours to minutes, enabling researchers to tackle significant computational challenges more effectively.

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.

This startup specializes in embedded AI technology that enhances the efficiency of electronic devices by utilizing a patent-pending variational auto-encoding neural network, eliminating the need for traditional power supplies. By integrating with existing smart home systems, their solution reduces electricity consumption while maintaining full functionality across various consumer and commercial devices.

Corticale develops neuroelectronic CMOS and bioelectronic devices that interface with neural tissue to monitor and stimulate brain activity. These devices provide precise, real-time data and therapeutic interventions for neurological disorders, enhancing treatment efficacy and patient outcomes.

FinalSpark is developing biocomputers that utilize biological neural networks grown from living neurons to achieve energy efficiency and scalability beyond traditional silicon-based AI systems. This technology addresses the high energy consumption and limited processing capabilities of current digital AI, enabling more powerful and sustainable computing solutions.

AxoMini is a pre-configured platform that cultures, monitors, and trains human neurons, accommodating up to 5 million neurons for diverse experiments in Biological/Organoid Intelligence. This system integrates data acquisition, stimulation, and incubation, enabling researchers to conduct neurobiology studies and drug screening without hardware or software constraints.

NeuronBasic designs and develops edge AI chips that enhance real-time data processing capabilities in resource-constrained environments. These chips address the limitations of traditional cloud computing by enabling faster decision-making and reduced latency for applications in IoT and autonomous systems.

Numelo Technologies manufactures semiconductors and specializes in Neuromorphic Chips designed for edge computing applications. These chips enhance processing efficiency and reduce latency in data-intensive tasks, addressing the limitations of traditional computing architectures in real-time environments.

Neuronspike Technologies develops brain-inspired chipsets using compute-in-memory architecture to enhance the performance of generative AI models, achieving up to 21 times faster processing than traditional processors. Their Neuronspike Moore chip delivers the throughput of four Nvidia A100 GPUs, addressing the limitations of memory bandwidth in AI computations.

Sagence AI develops analog in-memory compute technology that delivers high-performance AI inference with 100X lower power consumption and 20X lower costs compared to traditional digital solutions. This approach addresses the limitations of increasing digital chip densities and energy demands, making AI more economically viable and sustainable for widespread applications.

Sagence AI develops analog in-memory compute technology that delivers high-performance AI inference with 100X lower power consumption and 20X lower costs compared to traditional digital solutions. This approach addresses the limitations of increasing digital chip densities and energy demands, making AI more economically viable and sustainable for widespread applications.