Access pattern-based code compression for memory-constrained systems

Abstract

As compared to a large spectrum of performance optimizations, relatively less 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 addressing this constraint is to compress executable code and data as much as possible. While researchers on code compression have studied efficient hardware and software based code compression 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 article presents an application-sensitive 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 of the application 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 and evaluate several compression and decompression strategies that try to reduce memory requirements without excessively increasing the original instruction cycle counts. Some of our strategies make use of profile data, whereas others are fully automatic. Our experimental evaluation using seven applications from the MediaBench suite and three large embedded applications reveals that the proposed code compression strategy is very successful in practice. Our results also indicate that working at a basic block granularity, as opposed to a procedure granularity, is important for maximizing memory space savings. © 2008 ACM

A Survey Of Architectural Approaches for Data Compression in Cache and Main Memory Systems

“…[36,42]) or using compiler (e.g. [43,44]). Finally, these techniques have been used or evaluated in systems ranging from embedded systems [7,36,[45][46][47] to high-end servers e.g.…”

“…Afterwards, the most probable elements are coded with [8, 10, 12, 16, 17, 23, 28, 29, 35, 40, 48-50, 52, 53, 56, 63, 64, 66, 78, 82, 83, 88, 93] Zero-content compression [14,35,41,50,52,57,62,78,81] Frequent value compression (FVC) [35,52,59,60,67,70] Base delta immediate (BDI) compression [16,23,35,48,51,63,83,93,94] LZ and variants [6,7,9,17,19,20,22 [8,12] Compression in GPUs [89,93,94] Use of compiler [9,21,43,44,72,73,93] fewer number of bits than those which are least probable. Thus, Huffman coding uses a variablelength coding scheme.…”

“…Ozturk et al [44] present an application-aware compression strategy which utilizes the control flow graph (CFG) representation of the embedded program. In their work, all the basic blocks of the application are assumed to be compressed in the beginning.…”

Using Data Compression for Increasing Memory System Utilization

“…Very long instruction word processors are also considered in compression-related work [5]- [9]. Data compression is used to reduce storage requirements, bus bandwidth, and energy consumption [10]- [19]. Using a hardware compressor enables faster compression/ decompression, and this has been achieved by various techniques [10], [20]- [22].…”

A scalable and near-optimal representation of access schemes for memory management

“…The near-optimal computation of the resources is essential, as it directly affects the cost, area, and power consumption [Catthoor 1999]. For instance, many embedded systems have tight memory space constraints [Ozturk et al 2008], whereas the memory units contribute to the cost [Panda et al 1999], as the power is heavily dominated by array storage [Catthoor 1999]. …”