Energy-aware lossless data compression

By Kenneth C. Barr, Krste Asanovic. TOCS, 2006

426 citations recorded by the source corpus.

Wireless transmission of a single bit can require over 1000 times more energy than a single computation. It can therefore be beneficial to perform additional computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send it, there is a net energy savings and an increase in battery life for portable computers. This article presents a study of the energy savings possible by losslessly compressing data prior to transmission. A variety of algorithms were measured on a StrongARM SA-110 processor. This work demonstrates that, with several typical compression algorithms, there is a actually a net energy increase when compression is applied before transmission. Reasons for this increase are explained and suggestions are made to avoid it. One such energy-aware suggestion is asymmetric compression, the use of one compression algorithm on the transmit side and a different algorithm for the receive path. By choosing the lowest-energy compressor and decompressor on the test platform, overall energy to send and receive data can be reduced by 11% compared with a well-chosen symmetric pair, or up to 57% over the default symmetric zlib scheme.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/8bd35e7135d4ae6d79a9c96beb9987ec530e0fe8

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Citations in the corpus

Authenticated routing for ad hoc networks (2005, background): “However, the energy consumed by wireless communication is significantly higher; a single bit transmission consumes over 1000 times more energy than a single 32-bit computation [36].”
A survey on data compression in wireless sensor networks (2005, background): “The basic wireless sensor network configuration [1] is that a large number of sensor nodes are densely deployed over a sensor field.”
Data compression algorithms for energy-constrained devices in delay tolerant networks (2006, methodology): “Figure 2 shows the instruction counts and the default core memory usage of the algorithms evaluated in [5] versus our LZW implementation for sensor nodes, S-LZW.”
Multi-signatures in the plain public-Key model and a general forking lemma (2006, neutral): “Communicating even one bit of data uses signiﬁcantly more power than executing one 32-bit instruction [4].”
CAS-CNN: A deep convolutional neural network for image compression artifact suppression (2016, background): “Similar challenges are also seen in mobile devices storing data: size and cost constraints limit the amount of memory for data storage, and the energy available on such devices is depleted rapidly when writing to flash memory—so much that it pays off to apply compression before writing to flash memo”
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications (2014, background): “In [141], Barr and Asanović studied the tradeoff between energy consumption in data transmission and compression.”
Location-based compromise-tolerant security mechanisms for wireless sensor networks (2006, positive): “For example, wireless transmission of a bit can require over 1000 times more energy than a single 32-bit computation, as shown in [17].”