• Supports mainstream frameworks and the latest models capable of diverse deep learning tasks

• Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications!

• Provides a powerful open source quantizer that supports pruned and unpruned models quantization, calibration, and fine tuning.

• The AI profiler provides layer by layer analysis to help with bottlenecks

• The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud.

• Efficient and scalable IP cores can be customized to meet your needs of many different applications from a throughput, latency, power and lower-precision perspective.

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By converting the 32-bit floating-point weights and activations to fixed-point like INT8, the AI Quantizer can reduce the computing complexity without losing prediction accuracy. The fixed-point network model requires less memory bandwidth, thus providing faster speed and higher power efficiency than the floating-point model.

Maps the AI model to a high-efficient instruction set and data flow. Also performs sophisticated optimizations such as layer fusion, instruction scheduling, and reuses on-chip memory as much as possible.

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The performance profiler allows programmers to perform an in-depth analysis of the efficiency and utilization of your AI inference implementation.

The runtime provides a lightweight set of C++ and Python APIs. enabling easy application development. It also provides efficient task scheduling, memory management, and interrupt handling.