This work presents the first version of tinyMLPerf, a suite of benchmarks developed by the tinyML community to be used to compare tinyML hardware and software systems. The talk gives an insight into the development process behind the benchmark suite, describing the benchmark selection process, some of the design choices made, and the benchmarks selected for this first iteration consisting of four ML tasks: small vocabulary keyword spotting, binary image classification, small image classification, and anomaly detection using machine operating sounds. It will present the benchmark framework developed in a collaboration of MLCommons and EEMBC, the development of reference implementations on an ST platform to help submitters, the use of the benchmark to evaluate some performance-energy tradeoffs of a single solution, and some of the lessons learned during the process. The deployment of large numbers of sensors to monitor various environmental parameters (such as temperature, pressure, noise, pollutants, etc.) and the resulting availability of a large amount of data is motivating the use of machine learning (ML) algorithms including neural networks also on small devices with the goal of making the sensors “smarter” and thus enabling “intelligence at the edge”. ML techniques allow for more accurate analysis of complex sensor behaviors and interdependencies and can help quickly identify dangerous situations, such as the presence of poisonous gases in an indoor or outdoor environment. As the use of more complex algorithms spreads, a growing interest is observed in the scientific community toward a joint optimization of algorithms, software, and dedicated hardware for on-sensor data analysis (inference) on battery-operated low-power devices.įor the specific gas sensing application we address in the present contribution, a small Gated Recurrent Unit (GRU) is used to estimate gas concentrations in the air.
0 Comments
Leave a Reply. |