Integer and combinatorial optimization
Integer and combinatorial optimization
SUIF: an infrastructure for research on parallelizing and optimizing compilers
ACM SIGPLAN Notices
Exploiting dual data-memory banks in digital signal processors
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Simultaneous reference allocation in code generation for dual data memory bank ASIPs
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Quantifying the energy consumption of a pocket computer and a Java virtual machine
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Memory bank customization and assignment in behavioral synthesis
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Custom Memory Management Methodology: Exploration of Memory Organisation for Embedded Multimedia System Design
Data memory design considering effective bitwidth for low-energy embedded systems
Proceedings of the 15th international symposium on System Synthesis
Influence of Loop Optimizations on Energy Consumption of Multi-bank Memory Systems
CC '02 Proceedings of the 11th International Conference on Compiler Construction
DRAM Energy Management Using Sof ware and Hardware Directed Power Mode Control
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
Low-leakage asymmetric-cell SRAM
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Data Replication in Banked DRAMs for Reducing Energy Consumption
ISQED '06 Proceedings of the 7th International Symposium on Quality Electronic Design
ILP-Based energy minimization techniques for banked memories
ACM Transactions on Design Automation of Electronic Systems (TODAES)
| Hi-index | 0.00 |
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.