Exploiting Input Parameter Uncertainty for Reducing Datapath Precision of SPICE Device Models

Abstract

Double-precision computations operating on inputs with uncertainty margins can be compiled to lower precision fixed-point datapaths with no loss in output accuracy. We observe that ideal SPICE model equations based on device physics include process parameters which must be matched with real-world measurements on specific silicon manufacturing processes through a noisy data-fitting process. We expose this uncertainty information to the open-source FX-SCORE compiler to enable automated error analysis using the Gappa++ backend and hardware circuit generation using Vivado HLS. We construct an error model based on interval analysis to statically identify sufficient fixedpoint precision in the presence of uncertainty as compared to reference double-precision design. We demonstrate 1-16× LUT count improvements, 0.5-2.4× DSP count reductions and 0.9-4× FPGA power reduction for SPICE devices such as Diode, Level-1 MOSFET and an Approximate MOSFET designs. We generate confidence in our approach using Monte-Carlo simulations with auto-generated Matlab models of the SPICE device equations.