SuperBP: Design Space Exploration of Perceptron-Based Branch Predictors for Superconducting CPUs

Single Flux Quantum (SFQ) superconducting technology has a considerable advantage over CMOS in power and performance. SFQ CPUs can also help scale quantum computing technologies, as SFQ circuits can be integrated with qubits due to their amenability to a cryogenic environment. Recently, there have been significant developments in VLSI design automation tools, making it feasible to design pipelined SFQ CPUs. SFQ technology, however, is constrained by the number of Josephson Junctions (JJs) integrated into a single chip. Prior works focused on JJ-efficient SFQ datapath designs. Pipelined SFQ CPUs also require branch predictors that provide the best prediction accuracy for a given JJ budget. In this paper, we design and evaluate the original Perceptron branch predictor and a later variant named the Hashed Perceptron predictor in terms of their accuracy and JJ usage.