This is where a saved the day. The Toolkit to the Rescue
The go-to for Android and mobile-first applications, offering a suite of tools for quantization and hardware acceleration on mobile chips. deep learning deployment toolkit
This friction between "training" and "inference" is known as the . Bridging this gap is the primary function of the Deep Learning Deployment Toolkit (DLDT) . This is where a saved the day
Raw models are often too heavy for edge devices or cost-sensitive cloud environments. Optimization toolkits shrink the model size and boost speed without significantly sacrificing accuracy. Bridging this gap is the primary function of
Deploying to a smartphone or an IoT sensor requires a specialized toolkit focused on power efficiency and minimal memory footprint.
Deep learning models are typically trained using 32-bit floating-point numbers (FP32). FP32 offers high precision but demands high memory and computing power.