Dynamic Allocation for Scratch-Pad Memory using Compile-Time Decisions

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Dynamic Allocation for Scratch-Pad Memory using Compile-Time Decisions (2006)

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by Sumesh Udayakumaran , Angel Dominguez , Rajeev Barua

Venue:	the ACM Transactions on Embedded Computing Systems (TECS
Citations:	45 - 3 self

BibTeX

@ARTICLE{Udayakumaran06dynamicallocation,
    author = {Sumesh Udayakumaran and Angel Dominguez and Rajeev Barua},
    title = {Dynamic Allocation for Scratch-Pad Memory using Compile-Time Decisions},
    journal = {the ACM Transactions on Embedded Computing Systems (TECS},
    year = {2006},
    volume = {5},
    pages = {2006}
}

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Abstract

In this research we propose a highly predictable, low overhead and yet dynamic, memory allocation strategy for embedded systems with scratch-pad memory. A scratch-pad is a fast compiler-managed SRAM memory that replaces the hardware-managed cache. It is motivated by its better real-time guarantees vs cache and by its significantly lower overheads in energy consumption, area and overall runtime, even with a simple allocation scheme. Scratch-pad allocation primarily methods are of two types. First, software-caching schemes emulate the workings of a hardware cache in software. Instructions are inserted before each load/store to check the softwaremaintained cache tags. Such methods incur large overheads in runtime, code size, energy consumption and SRAM space for tags and deliver poor real-time guarantees just like hardware caches. A second category of algorithms partitions variables at compile-time into the two banks. However, a drawback of such static allocation schemes is that they do not account for dynamic program behavior. It is easy to see why a data allocation that never changes at runtime cannot achieve the full locality benefits of a cache. We propose a dynamic allocation methodology for global and stack data and program code that, (i) accounts for changing program requirements at runtime (ii) has no software-caching tags (iii) requires no run-time checks (iv) has extremely low overheads, and (v) yields 100 % predictable memory access times. In this method data

Keyphrases

scratch-pad memory compile-time decision dynamic allocation energy consumption low overhead hardware cache predictable memory access time hardware-managed cache static allocation scheme simple allocation scheme runtime cannot memory allocation strategy code size method data algorithm partition variable stack data poor real-time guarantee sram space full locality benefit run-time check dynamic program behavior fast compiler-managed sram memory software-caching scheme overall runtime scratch-pad allocation second category data allocation large overhead software-caching tag program requirement softwaremaintained cache tag load store program code real-time guarantee embedded system dynamic allocation methodology

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