Vitis™ AI is Xilinx’s development platform for AI inference on Xilinx hardware platforms, including both edge devices and Alveo cards. It consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease of use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP.
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.
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.
Vitis AI model zoo includes optimized deep learning models to speed up the deployment of deep learning inference on Xilinx™ platforms. These models cover different applications, including but not limited to ADAS/AD, video surveillance, robotics, data center, etc. You can get started with these pre-trained models to enjoy the benefits of deep learning acceleration.
The Xilinx AI Platform supports many AI/ML Models supported, as shown below. We are continually working to bring the latest models into our platform.
|General||Image classification||Googlenetv1, Resnet50, Resnet101, Resnet152 Inception v1, BN-inception, VGG16, SqueezeNet, Mobilenet , MobilenetV2|
|Object Detection||MobilnetV2-SSD, SSD, YOLO v2, YOLO v3, Tiny YOLO v2, Tiny YOLO v3|
|Face||Face detection||SSD, Densebox|
|Landmark Localization||Coordinates Regression|
|Face recognition||ResNet + Triplet / A-softmax Loss|
|Face attributes recognition||Classification and regression|
|Pose Estimation||Coordinates Regression|
|Object detection||SSD, RefineDet|
|Pedestrian Attributes Recognition||GoogleNet|
|Car Attributes Recognition||GoogleNet|
|Car Logo Recognition||Modified Densebox + GoogleNet|
|License Plate Detection||Modified DenseBox|
|License Plate Recognition||GoogleNet + Multi-task Learning|
|Object Detection||SSD, YOLOv2, YOLOv3|