Huffman Coding

Moffat, Alistair

doi:10.1145/3342555

Cited by 80 publications

(36 citation statements)

References 69 publications

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“…Huffman coding, compressed sparse columns (CSC) and compressed sparse row (CSR) have been applied to DNN compression [ 21 ]. Huffman coding [ 37 ] relies on iteratively combining two characters/values that have the smallest weights. The more times the character/value is repeated, the shorter the binary sequence is generated.…”

Section: Compression Algorithmsmentioning

confidence: 99%

Resource-constrained FPGA/DNN co-design

Zhang

Kouzani

2021

Neural Comput & Applic

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Deep neural networks (DNNs) have demonstrated super performance in most learning tasks. However, a DNN typically contains a large number of parameters and operations, requiring a high-end processing platform for high-speed execution. To address this challenge, hardware-and-software co-design strategies, which involve joint DNN optimization and hardware implementation, can be applied. These strategies reduce the parameters and operations of the DNN, and fit it into a low-resource processing platform. In this paper, a DNN model is used for the analysis of the data captured using an electrochemical method to determine the concentration of a neurotransmitter and the recoding electrode. Next, a DNN miniaturization algorithm is introduced, involving combined pruning and compression, to reduce the DNN resource utilization. Here, the DNN is transformed to have sparse parameters by pruning a percentage of its weights. The Lempel-Ziv-Welch algorithm is then applied to compress the sparse DNN. Next, a DNN overlay is developed, combining the decompression of the DNN parameters and DNN inference, to allow the execution of the DNN on a FPGA on the PYNQ-Z2 board. This approach helps avoid the need for inclusion of a complex quantization algorithm. It compresses the DNN by a factor of 6.18, leading to about 50% reduction in the resource utilization on the FPGA. Keywords Lempel-Ziv-Welch compression Á Deep neural network Á Hardware-and-software co-design Á Electrochemical sensing Á Field-programmable gate array Neural Computing and Applications

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Section: Compression Algorithmsmentioning

confidence: 99%

Resource-constrained FPGA/DNN co-design

Zhang

Kouzani

2021

Neural Comput & Applic

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“…They all use the probabilistic model to compress data. In terms of the compression effect, the compression efficiency of the two is close to the entropy of the information source, and there is no big difference (Moffat, 2019). In terms of computational complexity, arithmetic coding requires a lot of multiplication, addition and subtraction methods.…”

Section: The Analysis Of Probability Model Of Lwd Datamentioning

confidence: 99%

A lossless compression method for logging data while drilling

Song

Lian²,

Liu³

et al. 2021

Systems Science & Control Engineering

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Mud pulse transmission technology is widely used in logging while drilling (LWD), while its low data transmission efficiency has restricted the development of LWD technology for a long time. In order to improve the equivalent data transmission efficiency of LWD system, a data compression method, namely, Frame Prediction Huffman Coding (FPHC) with a group of frames, is proposed in this paper. Several factors including the data accuracy, the need for real-time transmission, and the existence of transmission errors are comprehensively considered in the proposed method. Through the probability statistical analysis of LWD data, it is proved that the data have a relatively stable probability distribution after predictive coding. In addition, the Huffman code table generated from predictive coding is universally applicable to other LWD data. Through data compression experiments on logging data, it is verified that the proposed method has higher compression efficiency compared with Huffman coding, Lempel-Ziv-Welch algorithm, adaptive Huffman coding and adaptive arithmetic coding. This lossless compression method will have a good application prospect because of its ability to prevent error diffusion, simple implementation and easy to be embedded in down-hole equipment.

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“…The most known techniques are arithmetic and Huffman codings. Huffman coding replaces words in a way that no word is a prefix of any other word in the system [111]. It creates a binary tree of nodes, which represents the symbols and their frequencies.…”

Section: Entropy-basedmentioning

confidence: 99%

State of the Art and Future Trends in Data Reduction for High-Performance Computing

2020

JSFI

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Research into data reduction techniques has gained popularity in recent years as storage capacity and performance become a growing concern. This survey paper provides an overview of leveraging points found in high-performance computing (HPC) systems and suitable mechanisms to reduce data volumes. We present the underlying theories and their application throughout the HPC stack and also discuss related hardware acceleration and reduction approaches. After introducing relevant use-cases, an overview of modern lossless and lossy compression algorithms and their respective usage at the application and file system layer is given. In anticipation of their increasing relevance for adaptive and in situ approaches, dimensionality reduction techniques are summarized with a focus on non-linear feature extraction. Adaptive approaches and in situ compression algorithms and frameworks follow. The key stages and new opportunities to deduplication are covered next. An unconventional but promising method is recomputation, which is proposed at last. We conclude the survey with an outlook on future developments.

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Huffman Coding

Cited by 80 publications

References 69 publications

Resource-constrained FPGA/DNN co-design

Resource-constrained FPGA/DNN co-design

A lossless compression method for logging data while drilling

State of the Art and Future Trends in Data Reduction for High-Performance Computing

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