Exploiting dual data-memory banks in digital signal processors

  • Authors:

  • Mazen A. R. Saghir;Paul Chow;Corinna G. Lee

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada;Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada;Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada

  • Venue:
  • Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
  • Year:
  • 1996

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Abstract

Over the past decade, digital signal processors (DSPs) have emerged as the processors of choice for implementing embedded applications in high-volume consumer products. Through their use of specialized hardware features and small chip areas, DSPs provide the high performance necessary for embedded applications at the low costs demanded by the high-volume consumer market. One feature commonly found in DSPs is the use of dual data-memory banks to double the memory system's bandwidth. When coupled with high-order data interleaving, dual memory banks provide the same bandwidth as more costly memory organizations such as a dual-ported memory. However, making effective use of dual memory banks remains difficult, especially for high-level language (HLL) DSP compilers.In this paper, we describe two algorithms --- compaction-based (CB) data partitioning and partial data duplication --- that we developed as part of our research into the effective exploitation of dual data-memory banks in HLL DSP compilers. We show that CB partitioning is an effective technique for exploiting dual data-memory banks, and that partial data duplication can augment CB partitioning in improving execution performance. Our results show that CB partitioning improves the performance of our kernel benchmarks by 13%-40% and the performance of our application benchmarks by 3%-15%. For one of the application benchmarks, partial data duplication boosts performance from 3% to 34%.