ESPRESSO-GPU: Blazingly Fast Two-Level Logic Minimization
Hitarth Kanakiaa, Mahdi Nazemib, Arash Fayyazic and Massoud Pedramd
Department of Electrical & Computer Engineering, University of Southern California, Los Angeles, CA, USA
akanakia@usc.edu
bmnazemi@usc.edu
cfayyazi@usc.edu
dpedram@usc.edu
ABSTRACT
Two-level logic minimization has found applications in new problems such as efficient realization of deep neural network inference. Important characteristics of these new applications are that they tend to produce very large Boolean functions (in terms of the supporting variables and/or initial sum of product representation) and have don’t-care-sets that are much larger in size than the on-set and off-set sizes. Applying conventional singlethreaded logic minimization heuristics to these problems becomes unwieldy. This work introduces ESPRESSO-GPU, a parallel version of ESPRESSO-II, which takes advantage of the computing capabilities of general-purpose graphics processors to achieve a huge speedup compared to existing serial implementations. Simulation results show that ESPRESSO-GPU achieves an average speedup of 97x compared to ESPRESSO-II.
