Efficient code density through look-up table compression

TL;DR: This paper introduces a novel and efficient hardware-supported approach that belongs to the group of statistical compression schemes as it is based on canonical Huffman coding, and is the first to also compress the necessary Look-up Tables that can become significant in size if the application is large and/or high compression is desired.

Abstract: Code density is a major requirement in embedded system design since it not only reduces the need for the scarce resource memory but also implicitly improves further important design parameters like power consumption and performance. Within this paper we introduce a novel and efficient hardware-supported approach that belongs to the group of statistical compression schemes as it is based on Canonical Huffman Coding. In particular, our scheme is the first to also compress the necessary Look-up Tables that can become significant in size if the application is large and/or high compression is desired. Our scheme optimizes the number of generated Look-up Tables to improve the compression ratio. In average, we achieve compression ratios as low as 49% (already including the overhead of the Lookup Tables). Thereby, our scheme is entirely orthogonal to approaches that take particularities of a certain instruction set architecture into account. We have conducted evaluations using a representative set of applications and have applied it to three major embedded processor architectures, namely ARM, MIPS and PowerPC.