Compiler-Decided Dynamic Memory Allocation for Scratch-Pad Based Embedded Systems
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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 methods primarily 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 software-maintained 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. 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 that is about to be accessed frequently is copied into the scratch-pad using compiler-inserted code at fixed and infrequent points in the program. Earlier data is evicted if necessary. When compared to an existing static allocation scheme, results show that our scheme reduces runtime by up to 39.8% and energy by up to 31.3% on average for our benchmarks, depending on the SRAM size used. The actual gain depends on the SRAM size, but our results show that close to the maximum benefit in run-time and energy is achieved for a substantial range of small SRAM sizes commonly found in embedded systems. Our comparison with a direct mapped cache shows that our method performs roughly as well as a cached architecture in runtime and energy while delivering better real-time benefits.