The reference model used is MobileNetEdgeTPU with 4M parameters, the dataset is ImageNet 2012 (224x224), and the quality target is 98% of FP32 (76.19% Top-1). Typical use cases include photo searches or text extraction. Image classification: This test involves inferring a label to apply to an input image.For more information, refer to the paper " MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It." It was created by MLCommons, a non-profit, open engineering consortium, to "deliver transparency and a level playing field for comparing ML systems, software, and solutions." MLPerf Mobile's first iteration provides an inference-performance benchmark for a handful of computer vision and natural language processing tasks. MLPerf Mobile: MLPerf Mobile is an open-source benchmark for testing mobile AI performance.It features complex geometry employing multiple render targets, reflections (cubic maps), mesh rendering, many deferred lighting sources, as well as bloom and depth of field in a post-processing pass. Manhattan ES 3.0/3.1: This test remains relevant given that modern games have already arrived at its proposed graphical fidelity and implement the same kinds of techniques.Most of these techniques will stress the shader compute capabilities of the processor. Specifically, the test offers really high polygon count geometry, hardware tessellation, high-resolution textures, global illumination and plenty of shadow mapping, copious particle effects, as well as bloom and depth of field effects. Currently, top mobile chipsets cannot sustain 30 frames per second.
Aztec Ruins: These tests are the most computationally heavy ones offered by GFXBench.The outputs are frames during test and frames per second (the other number divided by the test length, essentially), instead of a weighted score. Newer tests use Vulkan while legacy tests use OpenGL ES 3.1. Lots of onscreen effects and high-quality textures. GFXBench: Aims to simulate video game graphics rendering using the latest APIs.The final score is weighted according to the designer’s considerations, placing a large emphasis on integer performance (65%), then float performance (30%) and finally crypto (5%). The score breakdown gives specific metrics. GeekBench: A CPU-centric test that uses several computational workloads including encryption, compression (text and images), rendering, physics simulations, computer vision, ray tracing, speech recognition, and convolutional neural network inference on images.The final score is weighted according to the designer’s considerations. AnTuTu tests the CPU, GPU, and memory performance, while including both abstract tests and, as of late, relatable user experience simulations (for example, the subtest which involves scrolling through a ListView). 50% faster scalar accelerator, 2x faster tensor accelerator YoY.80% task reduction offload from Hexagon DSP.Hexagon 780 with Fused AI Accelerator architecture.
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