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

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 dat