Integer linear programming based energy optimization for banked DRAMs

Ozcan Ozturk, Mahmut Kandemir

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations


Memory system can be a major energy consumer in an embedded architecture. One way of reducing its energy consumption is banking, i.e., dividing available memory space into multiple, equally sized banks and placing an unused (idle) memory bank into a low-power operating mode. Prior work investigated code restructuring and data layout reorganization based approaches for increasing energy benefits that could be obtained from a banked memory architecture. This paper takes a different look at the problem of energy optimization in banked memory systems, and explores two compiler-assisted techniques that can co-exist with code/data transformations: data migration and data compression. The goal of data migration is to cluster data with similar access patterns in the same set of banks, thereby increasing the chances for utilizing low-power operating modes in a more effective manner. Data compression reduces the size of the data used by the application, and thus helps reduce the number of memory banks occupied by data. This in turn allows us place a larger number of banks into the low-power operating modes. We formulate the memory bank management problem as an ILP (integer linear programming) problem, and solve it using a publicly available ILP package.

Original languageEnglish (US)
Number of pages4
StatePublished - 2005
Event2005 ACM Great Lakessymposium on VLSI, GLSVLSI'05 - Chicago, IL, United States
Duration: Apr 17 2005Apr 19 2005


Other2005 ACM Great Lakessymposium on VLSI, GLSVLSI'05
Country/TerritoryUnited States
CityChicago, IL

All Science Journal Classification (ASJC) codes

  • General Engineering


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