The Problems
AI deployment spans diverse devices (e.g., microcontrollers, high-perf CPUs…).
Remote factory settings often face unreliable Wi-Fi.
Safety requires consistent performance and connectivity conditions.
Embedding language models in devices (e.g., smartphones) faces memory constraints (4–8GB RAM).
Additional challenges: battery consumption and hardware longevity.
On-device processing requires extra privacy by eliminating the need to send data to a server.
AI models often run locally on devices over cloud for real-time responses.
Real-time performance isn’t just hardware-dependent—efficiency is key.
Compression and fine-tuning enable models to handle workloads on limited-power devices.
The Solutions
Enabling Multimodal AI Platforms
Pruna guarantees workflow interoperability and compatibility with dynamic switching between models.
100% hardware agnostic, it is designed to prevent any hardware vendor lock-in.
Extreme Quantization
Pruna supports up to 1-bit quantization to reduce memory usage and computation, enabling AI on devices.
Additionally, quantization can be combined with other optimization methods to push the boundaries of AI efficiency.
Developing Continuous On-Device/Federated Learning
When the user interacts with the device, their behavior is continuously tracked (e.g., app usage, interactions) within strict privacy parameters
The model adjusts its focus dynamically, prioritizing areas that are most relevant to the user. Pruna brings higher accuracy for device tasks or higher compression for resource savings.
Manufacturing
Predictive maintenance, defects detection (e.g., surface scratches, assembly errors), real-time production optimization.
Automotive
AIDA, V2X communication, autonomous driving, driver monitoring systems (e.g., fatigue detection), and in-vehicle personalization.
Supply Chain
Demand forecasting, route optimization, warehouse automation (e.g., AI-powered picking and sorting), and anomaly detection in logistics.
A major German car manufacturer tasked us with evaluating 𝟱 𝗯𝗮𝘀𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝟯 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝘀𝗲𝘁𝘂𝗽𝘀 and measuring 𝟯 𝘁𝗼 𝟲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀. Pruna provided positive results across the board, no matter the hardware, all while maintaining accuracy and quality.