Interval simulation: raising the level of abstraction in architectural simulation

Detailed architectural simulators suffer from a long development cycle and extremely long evaluation times. This longstanding problem is further exacerbated in the multi-core processor era. Existing solutions address the simulation problem by either sampling the simulated instruction stream or by mapping the simulation models on FPGAs; these approaches achieve substantial simulation speedups while simulating performance in a cycle-accurate manner This paper proposes interval simulation which rakes a completely different approach: interval simulation raises the level of abstraction and replaces the core-level cycle-accurate simulation model by a mechanistic analytical model. The analytical model estimates core-level performance by analyzing intervals, or the timing between two miss events (branch mispredictions and TLB/cache misses); the miss events are determined through simulation of the memory hierarchy, cache coherence protocol, interconnection network and branch predictor By raising the level of abstraction, interval simulation reduces both development time and evaluation time. Our experimental results using the SPEC CPU2000 and PARSEC benchmark suites and the MS multi-core simulator show good accuracy up to eight cores (average error of 4.6% and max error of 11% for the multi-threaded full-system workloads), while achieving a one order of magnitude simulation speedup compared to cycle-accurate simulation. Moreover interval simulation is easy to implement: our implementation of the mechanistic analytical model incurs only one thousand lines of code. Its high accuracy, fast simulation speed and ease-of-use make interval simulation a useful complement to the architect's toolbox for exploring system-level and high-level micro-architecture trade-offs

Strategies to enhance the 3T1D-DRAM cell variability robustness beyond 22 nm

3T1D cell has been stated as a valid alternative to be implemented on L1 memory cache to substitute 6T, highly affected by device variability as technology dimensions are reduced. In this work, we have shown that 22 nm 3T1D memory cells present significant tolerance to high levels of device parameter fluctuation. Moreover, we have observed that when variability is considered the write access transistor becomes a significant detrimental element on the 3T1D cell performance. Furthermore, resizing and temperature control have been presented as some valid strategies in order to mitigate the 3T1D cell variability.Peer ReviewedPostprint (author's final draft