Code compression for low power embedded system design
Proceedings of the 37th Annual Design Automation Conference
Data and memory optimization techniques for embedded systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Design of an one-cycle decompression hardware for performance increase in embedded systems
Proceedings of the 39th annual Design Automation Conference
Low-energy off-chip SDRAM memory systems for embedded applications
ACM Transactions on Embedded Computing Systems (TECS)
Proceedings of the 35th annual ACM/IEEE international symposium on Microarchitecture
A hamming distance based VLIW/EPIC code compression technique
Proceedings of the 2004 international conference on Compilers, architecture, and synthesis for embedded systems
MiBench: A free, commercially representative embedded benchmark suite
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
A bitmask-based code compression technique for embedded systems
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Proceedings of the conference on Design, automation and test in Europe
Microprocessors & Microsystems
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Memory is one of the most significant detrimental factors in increasing the cost and area of embedded systems, especially assemiconductor technology scales down. Code compression techniques have been employed to reduce the memory requirement of the system without sacrificing its functionality. Bitmask-based code compression has been demonstrated to be a successful technique that produces low compression ratios while having a fast and simple decompression engine. However, the current approach requires dictionary sizes of +16K bytes to produce acceptable results, adding significant overhead to the system. In this paper, we develop a new hybrid encoding method that combines the traditional bitmask-based encoding and prefix-based Huffman encoding as well as a new dictionary selection technique based on a non-greedy algorithm. The combination of these two new methods reduces the compression ratio by 9-20% and performs well with small dictionary sizes.