Hardware Realization of Montgomery Multiplication with Radix-2

Author(s):  
Satyanarayana Vollala ◽  
N. Ramasubramanian ◽  
Utkarsh Tiwari
2021 ◽  
Vol 17 (3) ◽  
pp. 1-38
Author(s):  
Lauren Biernacki ◽  
Mark Gallagher ◽  
Zhixing Xu ◽  
Misiker Tadesse Aga ◽  
Austin Harris ◽  
...  

There is an increasing body of work in the area of hardware defenses for software-driven security attacks. A significant challenge in developing these defenses is that the space of security vulnerabilities and exploits is large and not fully understood. This results in specific point defenses that aim to patch particular vulnerabilities. While these defenses are valuable, they are often blindsided by fresh attacks that exploit new vulnerabilities. This article aims to address this issue by suggesting ways to make future defenses more durable based on an organization of security vulnerabilities as they arise throughout the program life cycle. We classify these vulnerability sources through programming, compilation, and hardware realization, and we show how each source introduces unintended states and transitions into the implementation. Further, we show how security exploits gain control by moving the implementation to an unintended state using knowledge of these sources and how defenses work to prevent these transitions. This framework of analyzing vulnerability sources, exploits, and defenses provides insights into developing durable defenses that could defend against broader categories of exploits. We present illustrative case studies of four important attack genealogies—showing how they fit into the presented framework and how the sophistication of the exploits and defenses have evolved over time, providing us insights for the future.


2010 ◽  
Vol 20 (06) ◽  
pp. 447-461 ◽  
Author(s):  
S. P. JOHNSTON ◽  
G. PRASAD ◽  
L. MAGUIRE ◽  
T. M. MCGINNITY

This paper presents an approach that permits the effective hardware realization of a novel Evolvable Spiking Neural Network (ESNN) paradigm on Field Programmable Gate Arrays (FPGAs). The ESNN possesses a hybrid learning algorithm that consists of a Spike Timing Dependent Plasticity (STDP) mechanism fused with a Genetic Algorithm (GA). The design and implementation direction utilizes the latest advancements in FPGA technology to provide a partitioned hardware/software co-design solution. The approach achieves the maximum FPGA flexibility obtainable for the ESNN paradigm. The algorithm was applied as an embedded intelligent system robotic controller to solve an autonomous navigation and obstacle avoidance problem.


2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Chayanit Bunsanit ◽  
Peerapong Uthansakul ◽  
Monthippa Uthansakul

So far, a wideband spatial beamformer has been proposed. This kind of beamformer has a major contribution as its weighting coefficients are real valued in which they can be simply realized by attenuators or amplifiers. However, so far, the range of attenuation or amplification is relatively large which is not practical for hardware realization. Therefore, this paper proposes a concept to reduce the range of weighting coefficients hence, the hardware realization becomes practical. In this paper, a full prototype of wideband spatial beamformer is constructed to reflect the true beamforming performance of the proposed refinement method. Its radiation patterns obtained from simulation and measurement are compared. As a result, we can reduce the attenuation or amplification range while some radiation characteristic is remained.


Author(s):  
Chao-Chung Cheng ◽  
Chia-Kai Liang ◽  
Yen-Chieh Lai ◽  
Homer H. Chen ◽  
Liang-Gee Chen

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