UC Davis Computer Architecture

Large-scale AI training and inference require hundreds of gigabytes to terabytes of DRAM with high peak to average utilization ratios, resulting in overprovisioning. In cloud computing, DRAM constitutes a significant share of the cost. Yet, as shown by recent articles, DRAM is heavily under utilized. Memory disaggregation is a solution to both these problems. With the advent of the CXL protocol, …

Jason Lowe-Power (jlowepower@ucdavis.edu)
5/30/2026

As Moore’s Law slows, superconducting electronics offer ultra-low-power, high-speed computation potential. This paper presents the first full-system superconducting architecture modeling in gem5, evaluating superconducting cores, caches, and interconnects under realistic workloads. We extend gem5 with cryogenic semiconductor (4 GHz) and superconducting (100 GHz) RISC-V cores and multi-level cache…