The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devices typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power.
TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware and software vendors to showcase their offerings.
TinyMLPerf is primarily intended to benchmark hardware rather than new network architectures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on their hardware.