Synthesis for testability by sequential redundancy removal using retiming

2002 ◽  
Author(s):  
H. Yotsuyanagi ◽  
S. Kajihara ◽  
K. Kinoshita
Keyword(s):  
Synthesis For Testability ◽  
Redundancy Removal ◽  
Sequential Redundancy

scholarly journals Testability Synthesis for Jumping Carry Adders

VLSI Design ◽  
2002 ◽  
Vol 14 (2) ◽  
pp. 155-169
Author(s):  
Chien-In Henry Chen ◽  
Mahesh Wagh
Keyword(s):  
High Speed ◽  
Design Method ◽  
Cmos Technology ◽  
Maximum Speed ◽  
Synthesis For Testability ◽  
Redundancy Removal ◽  
Area Reduction ◽  
Fundamental Relationship ◽  
Synthesis Strategy ◽  
Synthesis Methodology

Synthesis for testability ensures that the synthesized circuit is testable by exploring the fundamental relationship between don't care and redundancy. With the exploration of the relationship, redundancy removal can be applied to improve the testability, reduce the area and improve the speed of a synthesized circuit. The test generation problems have been adequately solved, therefore an innovative testability synthesis strategy is necessary for achieving the maximum fault coverage and area reduction for maximum speed. This paper presents a testability synthesis methodology applicable to a top–down design method based on the identification and removal of redundant faults. Emphasis has been placed on the testability synthesis of a high-speed binary jumping carry adder. A synthesized 32-bit testable adder implemented by a 1.2 μm CMOS technology performs addition in 4.09 ns. Comparing with the original synthesized circuit, redundancy removal yields a 100% testable design with a 15% improvement in speed and a 25% reduction in area.

scholarly journals Balancing Access Control and Privacy for Data Deduplication via Functional Encryption

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bo Mi ◽  
Ping Long ◽  
Yang Liu ◽  
Fengtian Kuang
Keyword(s):  
Access Control ◽  
Security Analysis ◽  
Functional Encryption ◽  
Data Deduplication ◽  
Paper Briefly ◽  
Storage Security ◽  
Redundancy Removal ◽  
Proof Of Ownership ◽  
Learning With Errors ◽  
And Storage

Data deduplication serves as an effective way to optimize the storage occupation and the bandwidth consumption over clouds. As for the security of deduplication mechanism, users’ privacy and accessibility are of utmost concern since data are outsourced. However, the functionality of redundancy removal and the indistinguishability of deduplication labels are naturally incompatible, which bring about a lot of threats on data security. Besides, the access control of sharing copies may lead to infringement on users’ attributes and cumbersome query overheads. To balance the usability with the confidentiality of deduplication labels and securely realize an elaborate access structure, a novel data deduplication scheme is proposed in this paper. Briefly speaking, we drew support from learning with errors (LWE) to make sure that the deduplication labels are only differentiable during the duplication check process. Instead of authority matching, the proof of ownership (PoW) is then implemented under the paradigm of inner production. Since the deduplication label is light-weighted and the inner production is easy to carry out, our scheme is more efficient in terms of computation and storage. Security analysis also indicated that the deduplication labels are distinguishable only for duplication check, and the probability of falsifying a valid ownership is negligible.

scholarly journals Redundancy Removal Adversarial Active Learning Based on Norm Online Uncertainty Indicator

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jifeng Guo ◽  
Zhiqi Pang ◽  
Wenbo Sun ◽  
Shi Li ◽  
Yu Chen
Keyword(s):  
Active Learning ◽  
Feature Vector ◽  
State Of The Art ◽  
Representation Learning ◽  
The State ◽  
Feature Representation ◽  
Learning Ability ◽  
Redundancy Removal ◽  
Previous State ◽  
Uncertainty Score

Active learning aims to select the most valuable unlabelled samples for annotation. In this paper, we propose a redundancy removal adversarial active learning (RRAAL) method based on norm online uncertainty indicator, which selects samples based on their distribution, uncertainty, and redundancy. RRAAL includes a representation generator, state discriminator, and redundancy removal module (RRM). The purpose of the representation generator is to learn the feature representation of a sample, and the state discriminator predicts the state of the feature vector after concatenation. We added a sample discriminator to the representation generator to improve the representation learning ability of the generator and designed a norm online uncertainty indicator (Norm-OUI) to provide a more accurate uncertainty score for the state discriminator. In addition, we designed an RRM based on a greedy algorithm to reduce the number of redundant samples in the labelled pool. The experimental results on four datasets show that the state discriminator, Norm-OUI, and RRM can improve the performance of RRAAL, and RRAAL outperforms the previous state-of-the-art active learning methods.

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