The UL Procyon AI Computer Vision Benchmark gives insights into how AI inference engines perform on your hardware in a Windows environment, helping you decide which engines to support to achieve the best performance. The benchmark features several AI inference engines from different vendors, with benchmark scores reflecting the performance of on-device inferencing operations.
The AI workloads used are common machine vision tasks such as image classification, image segmentation, object detection and super-resolution. These tasks are executed using a range of popular, state-of-the-art neural networks and can run on the device’s CPU, GPU or a dedicated AI accelerator for comparing hardware performance differences.
Software development kits (SDKs) used to measure the AI interference performance include:
- Microsoft ® Windows ML
- Qualcomm ® SNPE
- Intel® OpenVINO™
- NVIDIA® TensorRT™
- Apple® Core ML™
The benchmark includes both float- and integer-optimized versions of each model. Each model runs in turn on all compatible hardware in the device. Select the device and inference precision for each runtime to compare performance between integer and float models.
UL Procyon benchmarks use real applications whenever possible. Updates to those applications can affect your benchmark score. When comparing two or more systems, be sure to use the same version of each application on every system you test.