TinyML Use Cases

Tech
SaaS
UseCase
Use cases for TinyML offered by NinjaLABO
Author

Hiroshi Doyu

Published

July 2, 2024

Harnessing the Power of AI with NinjaLABO’s TinyML as-a-Service

In today’s competitive landscape, every company is leveraging AI or exploring its integration into their business operations. As AI models become increasingly sophisticated, the operational expenditures (OPEX) associated with utilizing expensive GPUs in cloud datacenters also rise. Moreover, large AI models often cannot be executed on small devices without relying on cloud GPUs.

NinjaLABO’s AI model compression (TinyML as-a-Service) addresses these challenges by offering versatile solutions applicable across various industries. Below, we explore specific focus areas and use cases where these solutions are particularly relevant:

IoT and Smart Cities

  • Data Types: Sensor data (temperature, humidity, air quality, noise levels), traffic data, utility usage data.
  • Use Cases: Predictive maintenance, energy management, traffic optimization, environmental monitoring.
    • Advantage: Typically, 99% of data transmission is redundant, wasting network bandwidth and storage. Local AI execution with TinyML eliminates this inefficiency by transmitting data only when anomalies occur, thus optimizing communication and storage.

Healthcare and Wearables

  • Data Types: Biometric data (heart rate, activity levels, sleep patterns), medical imaging data.
  • Use Cases: Health monitoring, early disease detection, personalized healthcare, fitness tracking.
    • Advantage: Regulatory constraints often prohibit uploading private data to public clouds. Local AI execution with TinyML ensures that only processed, non-sensitive data is uploaded, preserving privacy.

Industrial Automation

  • Data Types: Machine performance data, operational data, maintenance logs.
  • Use Cases: Predictive maintenance, process optimization, quality control.
    • Advantage: Similar to IoT use cases, local AI execution minimizes unnecessary data transmission, enhancing efficiency and security.

Agriculture

  • Data Types: Soil moisture levels, weather data, crop health data.
  • Use Cases: Precision farming, crop monitoring, irrigation management.
    • Advantage: Agricultural fields often extend beyond network coverage. With TinyML, AI can be executed locally, enabling smart farming even in off-the-grid areas. This benefit extends to other off-the-grid network and battery-powered applications.

Automotive and Mobility

  • Data Types: Vehicle performance data, driver behavior data, traffic data.
  • Use Cases: Autonomous driving, fleet management, driver safety systems.
    • Advantage: Real-time response is critical. Local AI execution with TinyML ensures immediate processing, which is crucial for safety and efficiency in automotive applications.

Security and Surveillance

  • Data Types: Video feeds, audio recordings, motion sensor data.
  • Use Cases: Intrusion detection, anomaly detection, crowd monitoring.
    • Advantage: Local data execution enhances security by reducing the need to transmit sensitive data, mitigating potential breaches.

These examples highlight the broad applicability of NinjaLABO’s solutions. By focusing on specific use cases within these industries, NinjaLABO can tailor its services to meet the unique needs and challenges of each sector, providing efficient, scalable, and impactful TinyML solutions.