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.Since branch predictors, to date, have not been designed for SFQ CPUs, we first design a baseline predictor built using nondestructive readout (NDRO) cells for storing the perceptron weights. Given that NDRO cells are JJ intensive, we propose an enhanced JJ-efficient design, called SuperBP, that uses high-capacity destructive readout (HC-DRO) cells to store perceptron weights. HC-DRO is a recently introduced multi-bit fluxon storage cell that stores 2 bits per cell. HC-DRO cells double the weight storage density over basic DRO cells to improve prediction accuracy for a given JJ count. However, naive integration of HC-DRO with SFQ logic is inefficient as HC-DRO cells store multiple fluxons in a single cell, which needs a decoding step on a read and an encoding step on a write. SuperBP presents novel inference and prediction update circuits for the Perceptron predictor that can directly operate on the native 2-bit HC-DRO weights without decoding and encoding, thereby reducing the JJ use. SuperBP reduces the JJ count by 39% compared to the NDRO-based design.We evaluate the performance of Perceptron and its hashed variants with the HC-DRO cell design using a range of benchmarks, including several SPEC CPU 2017, mobile, and server traces from the 5th Championship Branch Predictor competition. Our evaluation shows that for a given JJ count, the basic Perceptron variant of SuperBP provides better accuracy than the hashed variant. The hashed variant uses multiple weight tables, each of which needs its own access decoder, and decoder designs in SFQ consume a significant number of JJs. Thus, the hashed variant of SuperBP wastes the JJ budget for accessing multiple tables, leaving a smaller weight storage capacity, which compromises prediction accuracy. The basic Perceptron variant of SuperBP improves prediction accuracy by 13.6% over the hashed perceptron variant for an exemplar 30K JJ budget.CCS CONCEPTS• Hardware → Superconducting circuits ; • Computer systems organization → Architectures.