With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been increased.This paper presents a data variable allocation(DVA)algorithm based on the genetic a...With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been increased.This paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm for NVM-based SPM to prolong the lifetime.The lifetime can be formulated indirectly as the write counts on each SPM address.Since the differences between global variables and stack variables,our optimization model has three constraints.The constraints of the central processing unit(CPU)utilization and size are used for all variables,while no-overlay constraint is only used for stack variables.To satisfy the constraints of the optimization model,we use the greedy strategy to generate the initial population which can determine whether data variables are allocated to SPM and distribute them evenly on SPM addresses.Finally,we use the Mälardalen worst case executive time(WCET)benchmark to evaluate our algorithm.The experimental results show that the DVA algorithm can not only obtain close-to-optimal solutions,but also prolong the lifetime by 9.17% on average compared with SRAM-based SPM.展开更多
基金supported by the Research Fund of National Key Laboratory of Computer Architecture under Grant No.CARCH201501the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing under Grant No.2016A09.
文摘With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been increased.This paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm for NVM-based SPM to prolong the lifetime.The lifetime can be formulated indirectly as the write counts on each SPM address.Since the differences between global variables and stack variables,our optimization model has three constraints.The constraints of the central processing unit(CPU)utilization and size are used for all variables,while no-overlay constraint is only used for stack variables.To satisfy the constraints of the optimization model,we use the greedy strategy to generate the initial population which can determine whether data variables are allocated to SPM and distribute them evenly on SPM addresses.Finally,we use the Mälardalen worst case executive time(WCET)benchmark to evaluate our algorithm.The experimental results show that the DVA algorithm can not only obtain close-to-optimal solutions,but also prolong the lifetime by 9.17% on average compared with SRAM-based SPM.
