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Due to increasing amounts of data and compute resources, deep learningachieves many successes in various domains. The application of deep learning onthe mobile and embedded devices is taken more and more attentions, benchmarkingand ranking the AI abilities of mobile and embedded devices becomes an urgentproblem to be solved. Considering the model diversity and framework diversity,we propose a benchmark suite, AIoTBench, which focuses on the evaluation of theinference abilities of mobile and embedded devices. AIoTBench covers threetypical heavy-weight networks: ResNet50, InceptionV3, DenseNet121, as well asthree light-weight networks: SqueezeNet, MobileNetV2, MnasNet. Each network isimplemented by three frameworks which are designed for mobile and embeddeddevices: Tensorflow Lite, Caffe2, Pytorch Mobile. To compare and rank the AIcapabilities of the devices, we propose two unified metrics as the AI scores:Valid Images Per Second (VIPS) and Valid FLOPs Per Second (VOPS). Currently, wehave compared and ranked 5 mobile devices using our benchmark. This list willbe extended and updated soon after.

Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices