• 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.

With world-leading model compression technology, we can reduce model complexity by 5x to 50x with minimal accuracy impact. Deep Compression takes the performance of your AI inference to the next level.

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.