Early stage real-time SoC power estimation using RTL instrumentation

Early stage power estimation is critical for SoC architecture exploration and validation in modern VLSI design, but real-time, long time interval and accurate estimation is still challenging for system-level estimation and software/hardware tuning. This work proposes a model abstraction approach for real-time power estimation in the manner of machine learning. The singular value decomposition (SVD) technique is exploited to abstract the principle components of relationship between register toggling profile and accurate power waveform. The abstracted power model is automatically instrumented to RTL implementation and synthesized into FPGA platform for real-time power estimation by instrumenting the register toggling profile. The prototype implementation on three IP cores predicts the cycle-by-cycle power dissipation within 5% accuracy loss compared with a commercial power estimation tool.