NDLI: Area and Power Reduction of Embedded DSP Systems using Instruction Compression and Re-configurable Encoding

Content Provider	Springer Nature Link
Author	Chandar, Subash Mehendale, Mahesh Govindarajan, R.
Copyright Year	2006
Abstract	In embedded control applications, system cost and power/energy consumption are key considerations. In such applications, program memory forms a significant part of the chip area. Hence reducing code size reduces the system cost significantly. A significant part of the total power is consumed in fetching instructions from the program memory. Hence reducing instruction fetch power has been a key target for reducing power consumption. To reduce the cost and power consumption, embedded systems in these applications use application specific processors that are fine tuned to provide better solutions in terms of code density, and power consumption. Further fine tuning to suit each particular application in the targeted class can be achieved through reconfigurable architectures. In this paper, we propose a reconfiguration mechanism, called Instruction Re-map Table, to re-map the instructions to shorter length code words. Using this mechanism, frequently used set of instructions can be compressed. This reduces code size and hence the cost. Secondly, we use the same mechanism to target power reduction by encoding frequently used instruction sequences to Gray codes. Such encodings, along with instruction compression, reduce the instruction fetch power. We enhance Texas Instruments DSP core TMS320C27x to incorporate this mechanism and evaluate the improvements on code size and instruction fetch energy using real life embedded control application programs as benchmarks. Our scheme reduces the code size by over 10% and the energy consumed by over 40%.
Starting Page	245
Ending Page	267
Page Count	23
File Format	PDF
ISSN	09225773
Journal	Journal of Signal Processing Systems
Volume Number	44
Issue Number	3
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2006-07-25
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	code compression embedded DSP systems energy reduction re-configurable architecture Signal, Image and Speech Processing Circuits and Systems Electrical Engineering Image Processing and Computer Vision Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Information Systems Electrical and Electronic Engineering

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