Revisiting a Masked Lookup-Table Compression Scheme

2017 ◽  
pp. 369-383
Author(s):  
Srinivas Vivek
Keyword(s):  
Lookup Table ◽  
Compression Scheme

scholarly journals Second-Order Masked Lookup Table Compression Scheme

2020 ◽  
pp. 129-153
Author(s):  
Annapurna Valiveti ◽  
Srinivas Vivek
Keyword(s):  
Second Order ◽  
Lookup Table ◽  
Side Channel ◽  
Compression Scheme ◽  
Trade Off ◽  
Leakage Model ◽  
Compression Parameter ◽  
Order Scheme ◽  
Software Implementations ◽  
Iot Devices

Masking by lookup table randomisation is a well-known technique used to achieve side-channel attack resistance for software implementations, particularly, against DPA attacks. The randomised table technique for first- and second-order security requires about m•2n bits of RAM to store an (n,m)-bit masked S-box lookup table. Table compression helps in reducing the amount of memory required, and this is useful for highly resource-constrained IoT devices. Recently, Vadnala (CT-RSA 2017) proposed a randomised table compression scheme for first- and second-order security in the probing leakage model. This scheme reduces the RAM memory required by about a factor of 2l, where l is a compression parameter. Vivek (Indocrypt 2017) demonstrated an attack against the second-order scheme of Vadnala. Hence achieving table compression at second and higher orders is an open problem.In this work, we propose a second-order secure randomised table compression scheme which works for any (n,m)-bit S-box. Our proposal is a variant of Vadnala’s scheme that is not only secure but also significantly improves the time-memory trade-off. Specifically, we improve the online execution time by a factor of 2n−l. Our proposed scheme is proved 2-SNI secure in the probing leakage model. We have implemented our method for AES-128 on a 32-bit ARM Cortex processor. We are able to reduce the memory required to store a randomised S-box table for second-order AES-128 implementation to 59 bytes.

A Test Data Compression Scheme Based on Mixed Fixed and Variable Length Coding

2009 ◽  
Vol 31 (10) ◽  
pp. 1826-1834 ◽  
Author(s):  
Wen-Fa ZHAN ◽  
Hua-Guo LIANG ◽  
Feng SHI ◽  
Zheng-Feng HUANG
Keyword(s):  
Data Compression ◽  
Test Data ◽  
Variable Length ◽  
Test Data Compression ◽  
Compression Scheme ◽  
Variable Length Coding

A novel joint data-hiding and compression scheme based on SMVQ and image inpainting

2020 ◽  
Author(s):  
S. Poonguzhali ◽  
Avinash Sharma ◽  
V. Vedanarayanan ◽  
A. Aranganathan ◽  
T. Gomathi ◽  
...  
Keyword(s):  
Data Hiding ◽  
Image Inpainting ◽  
Compression Scheme

LOOKUP TABLE-BASED ADAPTIVE BODY BIASING OF MULTIPLE MACROS FOR PROCESS VARIATION COMPENSATION AND LOW LEAKAGE

2010 ◽  
Vol 19 (07) ◽  
pp. 1449-1464 ◽  
Author(s):  
BYUNGHEE CHOI ◽  
YOUNGSOO SHIN
Keyword(s):  
Supply Voltage ◽  
Process Variation ◽  
Power Saving ◽  
Lookup Table ◽  
Low Leakage ◽  
Fine Grain ◽  
Body Biasing ◽  
Block Level ◽  
Adaptive Body Bias ◽  
Body Bias

A reduced supply voltage must be accompanied by a reduced threshold voltage, which makes this approach to power saving susceptible to process variation in transistor parameters, as well as resulting in increased subthreshold leakage. While adaptive body biasing is efficient for both compensating process variation and suppressing leakage current, it suffers from a large overhead of control circuit. Most body biasing circuits target an entire chip, which causes excessive leakage of some blocks and misses the chance of fine grain control. We propose a new adaptive body biasing scheme, based on a lookup table for independent control of multiple functional blocks on a chip, which controls leakage and also compensates for process variation at the block level. An adaptive body bias is applied to blocks in active mode and a large reverse body bias is applied to blocks in standby mode. This is achieved by a central body bias controller, which has a low overhead in terms of area, delay, and power consumption. The problem of optimizing the required set of bias voltages is formulated and solved. A design methodology for semicustom design using standard-cell elements is developed and verified with benchmark circuits.

scholarly journals A data reduction and compression description for high throughput time-resolved electron microscopy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Abhik Datta ◽  
Kian Fong Ng ◽  
Deepan Balakrishnan ◽  
Melissa Ding ◽  
See Wee Chee ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Temporal Resolution ◽  
Data Reduction ◽  
Accurate Estimation ◽  
Data Streaming ◽  
Compression Scheme ◽  
Raw Data ◽  
Time Resolved ◽  
Detection And Counting ◽  
Spatio Temporal

AbstractFast, direct electron detectors have significantly improved the spatio-temporal resolution of electron microscopy movies. Preserving both spatial and temporal resolution in extended observations, however, requires storing prohibitively large amounts of data. Here, we describe an efficient and flexible data reduction and compression scheme (ReCoDe) that retains both spatial and temporal resolution by preserving individual electron events. Running ReCoDe on a workstation we demonstrate on-the-fly reduction and compression of raw data streaming off a detector at 3 GB/s, for hours of uninterrupted data collection. The output was 100-fold smaller than the raw data and saved directly onto network-attached storage drives over a 10 GbE connection. We discuss calibration techniques that support electron detection and counting (e.g., estimate electron backscattering rates, false positive rates, and data compressibility), and novel data analysis methods enabled by ReCoDe (e.g., recalibration of data post acquisition, and accurate estimation of coincidence loss).

scholarly journals Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module

2021 ◽  
Vol 11 (10) ◽  
pp. 4614
Author(s):  
Xiaofei Chao ◽  
Xiao Hu ◽  
Jingze Feng ◽  
Zhao Zhang ◽  
Meili Wang ◽  
...  
Keyword(s):  
Disease Control ◽  
Classification Accuracy ◽  
Mobile Terminal ◽  
Disease Classification ◽  
Memory Usage ◽  
Compression Scheme ◽  
Accurate Identification ◽  
Average Classification Accuracy ◽  
Network Compression ◽  
Control And Management

The fast and accurate identification of apple leaf diseases is beneficial for disease control and management of apple orchards. An improved network for apple leaf disease classification and a lightweight model for mobile terminal usage was designed in this paper. First, we proposed SE-DEEP block to fuse the Squeeze-and-Excitation (SE) module with the Xception network to get the SE_Xception network, where the SE module is inserted between the depth-wise convolution and point-wise convolution of the depth-wise separable convolution layer. Therefore, the feature channels from the lower layers could be directly weighted, which made the model more sensitive to the principal features of the classification task. Second, we designed a lightweight network, named SE_miniXception, by reducing the depth and width of SE_Xception. Experimental results show that the average classification accuracy of SE_Xception is 99.40%, which is 1.99% higher than Xception. The average classification accuracy of SE_miniXception is 97.01%, which is 1.60% and 1.22% higher than MobileNetV1 and ShuffleNet, respectively, while its number of parameters is less than those of MobileNet and ShuffleNet. The minimized network decreases the memory usage and FLOPs, and accelerates the recognition speed from 15 to 7 milliseconds per image. Our proposed SE-DEEP block provides a choice for improving network accuracy and our network compression scheme provides ideas to lightweight existing networks.

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