PFS - Using ML for Back-Annotating Low-Level Effects in a System-Level Framework

The work presented here is on back-annotation of physical properties into components of an embedded system for system-level simulation and facilitating fast design space exploration. Two important properties, power and noise are considered in this thesis. A general ML-based framework is proposed for both power and noise back-annotation. This framework removes the expensive low-level simulations and performs a one-time offline effort for low-level property characterization. The actual data from characterization generates the dataset for training ML-based models. We evaluate several ML techniques on this task like multi-layer perceptron, convolutional neural network, gradient boosting, and LSTM. The trained model will be implemented in a fast SystemC surrogate model that speeds up the simulation time for system design space exploration. In this paper the methodology will be explained for interconnect crosstalk back-annotation. A crosstalk model for a RISC-V like processor interconnects is implemented and evaluated as a case study. The same methodology is used for backannotating and evaluating processor power at system level.