Access Pattern-Based Code Compression for Memory-Constrained Embedded Systems

TL;DR

This paper introduces a control flow graph-based code compression method for embedded systems that predicts and decompresses only the necessary code blocks to optimize memory usage while balancing performance.

Contribution

It presents a novel application-aware compression strategy that dynamically manages code blocks based on program behavior, improving memory efficiency in embedded systems.

Findings

Reduces memory footprint by selectively decompressing code blocks.

Balances compression effectiveness with minimal performance overhead.

Demonstrates improved memory management in embedded applications.

Abstract

As compared to a large spectrum of performance optimizations, relatively little effort has been dedicated to optimize other aspects of embedded applications such as memory space requirements, power, real-time predictability, and reliability. In particular, many modern embedded systems operate under tight memory space constraints. One way of satisfying these constraints is to compress executable code and data as much as possible. While research on code compression have studied efficient hardware and software based code strategies, many of these techniques do not take application behavior into account, that is, the same compression/decompression strategy is used irrespective of the application being optimized. This paper presents a code compression strategy based on control flow graph (CFG) representation of the embedded program. The idea is to start with a memory image wherein all basic blocks are compressed, and decompress only the blocks that are predicted to be needed in the near future. When the current access to a basic block is over, our approach also decides the point at which the block could be compressed. We propose several compression and decompression strategies that try to reduce memory requirements without excessively increasing the original instruction cycle counts.