Aizip, Inc.

Cupertino,  CA 
United States
  • Booth: ST4

Explore Aizip's smart and efficient TinyML solutions!

Aizip, Inc., a Silicon Valley-based company, specializes in AI models for IoT applications, offering design services with high performance, quick turnaround, and excellent ROI. As a leader in the rapidly growing TinyML market, Aizip provides intelligent, automated, and connected solutions for privacy protection, real-time detection, and low power consumption. The highly qualified founding team brings extensive experience from top research universities and AI powerhouses. Aizip offers four series of TinyML models – Aizip intelligent vision, audio, time-series, and module (AIA, AIT, and AIM) for diverse applications, including video classification, speaker identification, and ECG readings. Collaborating with leading IC companies, Aizip delivers comprehensive solutions and a customer-centric approach. Their AI Design-as-a-Service (DaaS) model allows customers to access top-notch performance with a short development cycle.

Brands: Aizip, your AI partner, drives IoT innovation with high-performing TinyML models. We deliver intelligent, automated solutions, ensuring real-time detection, privacy, and low power use.


  • SARAD-T for industrial preventive maintenance
    Aizip's Scalable and Robust Anomaly Detection (SARAD-T): A game-changer in preventive maintenance. Versatile, efficient, and capable of real-time local anomaly detection for diverse industrial needs....

  • Preventive maintenance plays a crucial role in optimizing the efficiency and longevity of industrial equipment. It enables businesses to proactively identify potential equipment failures and maintenance needs before they lead to costly downtime or hazardous situations. In a rapidly evolving industrial landscape, preventive maintenance has become an indispensable tool for companies seeking to maintain a competitive edge, improve worker safety, and contribute to a sustainable future.

    Aizip has collaborated with Analog Device to create SARAD-T – a Scalable And Robust Anomaly Detection on Time-series technology designed for a broad spectrum of preventive maintenance applications. Utilizing neural networks, SARAD-T accurately detects abnormal operating conditions in various industrial equipment. By implementing Aizip's proprietary efficient neural network architecture, SARAD-T operates locally on Analog Devices' MAX32655 chip, processing sensor data in real-time. This eliminates the need for continuous transmission of vast sensor data to the cloud, significantly cutting operating costs and facilitating seamless system integration and deployment.

    The primary challenge of incorporating neural network algorithms into preventive maintenance lies in scalability. Numerous industrial applications require preventive maintenance, and operating conditions vary across different environments and equipment lifespans. Typically, manual data collection and model re-training are required for each new configuration. However, SARAD-T offers a versatile "one-for-many" solution. For a new application, it only requires a brief “registration” period, during which the model is exposed to data under normal operating conditions. Without further re-training, the model can then detect when abnormal data patterns occur. This enables rapid integration of the technology across diverse applications.

    SARAD-T stands out as a unique solution in the realm of preventive maintenance, owing to two key aspects: scalability and edge-processing.

    In terms of scalability, SARAD-T overcomes the typical challenges faced by conventional neural network algorithms, which often require manual data collection and model re-training for each new application. Instead, SARAD-T boasts a versatile "one-for-many" solution. To integrate SARAD-T into a new application, only a brief "registration" period is necessary during which the neural network model is exposed to data under normal operating conditions. Following this, the model can accurately detect when abnormal data patterns occur without any additional re-training. This flexibility enables the technology to be rapidly integrated into a diverse range of applications, making it a highly efficient and adaptable option for industries with varying needs.

    As for edge-processing, SARAD-T processes sensor data locally on Analog Devices' MAX32655 chip, eliminating the need to rely on cloud services. This real-time, on-device processing approach significantly reduces operating costs and latency, ensuring a more efficient and streamlined system. By bypassing the constant transmission of vast amounts of sensor data to the cloud, SARAD-T fosters a more secure and responsive environment for preventive maintenance applications.

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