Minimal placement of bank selection instructions for partitioned memory architectures

  • Authors:

  • Bernhard Scholz;Bernd Burgstaller;Jingling Xue

  • Affiliations:

  • The University of Sydney, Sydney, Australia;Yonsei University, Seoul, Korea;University of New South Wales, Sydney, Australia

  • Venue:
  • ACM Transactions on Embedded Computing Systems (TECS)
  • Year:
  • 2008

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Abstract

We have devised an algorithm for minimal placement of bank selections in partitioned memory architectures. This algorithm is parameterizable for a chosen metric, such as speed, space, or energy. Bank switching is a technique that increases the code and data memory in microcontrollers without extending the address buses. Given a program in which variables have been assigned to data banks, we present a novel optimization technique that minimizes the overhead of bank switching through cost-effective placement of bank selection instructions. The placement is controlled by a number of different objectives, such as runtime, low power, small code size or a combination of these parameters. We have formulated the minimal placement of bank selection instructions as a discrete optimization problem that is mapped to a partitioned boolean quadratic programming (PBQP) problem. We implemented the optimization as part of a PIC Microchip backend and evaluated the approach for several optimization objectives. Our benchmark suite comprises programs from MiBench and DSPStone plus a microcontroller real-time kernel and drivers for microcontroller hardware devices. Our optimization achieved a reduction in program memory space of between 2.7 and 18.2&percent;, and an overall improvement with respect to instruction cycles between 5.0 and 28.8&percent;. Our optimization achieved the minimal solution for all benchmark programs. We investigated the scalability of our approach toward the requirements of future generations of microcontrollers. This study was conducted as a worst-case analysis on the entire MiBench suite. Our results show that our optimization (1) scales well to larger numbers of memory banks, (2) scales well to the larger problem sizes that will become feasible with future microcontrollers, and (3) achieves minimal placement for more than 72&percent; of all functions from MiBench.