OSS + ELC EU 2026

Eliminating cloud dependency and making connectivity optional is becoming essential for applications where data privacy, low latency, and reliability are critical. Running AI locally allows the system to respond immediately and keeps sensitive data on device which guarantees security. However, the cost is high. It introduces significant challenges due to computing, memory, and power constraints.

This session explores optimization techniques and algorithms mandatory for deploying vision, audio, and language models on resource-constrained edge devices. We will dive into methods such as quantization, model compression, and graph optimization, and show how to balance latency, accuracy, and energy efficiency.

Using widely adopted open-source frameworks like PyTorch and TensorFlow, along with intermediate ecosystems such as ONNX, we will demonstrate how to efficiently deploy from training a model to highly optimized edge inference. We will provide insight into making AI models smaller, faster, and more efficient with minimal impact on their accuracy, allowing them to run on microprocessor and microcontroller-class devices.