Bitmask-based control word compression for NISC architectures
Proceedings of the 19th ACM Great Lakes symposium on VLSI
A universal placement technique of compressed instructions for efficient parallel decompression
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Test data compression using efficient bitmask and dictionary selection methods
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Decoding-aware compression of FPGA bitstreams
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Test Data Compression Using Selective Sparse Storage
Journal of Electronic Testing: Theory and Applications
Test data compression using interval broadcast scan for embedded cores
Microelectronics Journal
Microprocessors & Microsystems
Synergistic integration of code encryption and compression in embedded systems
Proceedings of the great lakes symposium on VLSI
FPGA bitstream compression and decompression using LZ and golomb coding (abstract only)
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
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Embedded systems are constrained by the available memory. Code-compression techniques address this issue by reducing the code size of application programs. It is a major challenge to develop an efficient code-compression technique that can generate substantial reduction in code size without affecting the overall system performance. We present a novel code-compression technique using bitmasks, which significantly improves the compression efficiency without introducing any decompression penalty. This paper makes three important contributions. 1) It develops an efficient bitmask-selection technique that can create a large set of matching patterns. 2) It develops an efficient dictionary-selection technique based on bitmasks. 3) It proposes a dictionary-based code-compression algorithm using the bitmask- and dictionary-selection techniques that can significantly reduce the memory requirement. To demonstrate the usefulness of our approach, we have performed code compression using applications from various domains and compiled for a wide variety of architectures. Our approach outperforms the existing dictionary-based techniques by an average of 20%, giving a compression ratio of 55%-65%.