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High Level Synthesis Introduction To Chip And System Design

Hardware security and trust have become a pressing issue during the last two decades due to the globalization of the semiconductor supply chain and ubiquitous network connection of computing devices. Computing hardware is now an attractive attack surface for launching powerful cross-layer security attacks, allowing attackers to infer secret information, hijack control flow, compromise system root-of-trust, steal intellectual property (IP), and fool machine learners. On the other hand, security practitioners have been making tremendous efforts in developing protection techniques and design tools to detect hardware vulnerabilities and fortify hardware design against various known hardware attacks. This article presents an overview of hardware security and trust from the perspectives of threats, countermeasures, and design tools. By introducing the most recent advances in hardware security research and developments, we aim to motivate hardware designers and electronic design automation tool developers to consider the new challenges and opportunities of incorporating an additional dimension of security into robust hardware design, testing, and verification.

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An Overview of Hardware Security and Trust: Threats, Countermeasures, and Design Tools

In this paper, we analyze how stabilizing the variance of intensity-dependent quantum noise in digital mammograms can significantly improve the computerized detection of microcalcifications (MCs). These lesions appear on mammograms as tiny deposits of calcium smaller than 20 pixels in diameter. At this scale, high frequency image noise is dominated by quantum noise, which in raw mammograms can be described with a square-root noise model. Under this assumption, we derive an adaptive variance stabilizing transform (VST) that stabilizes the noise to unitary standard deviation in all the images. This is achieved by estimating the noise characteristics from the image at hand. We tested the adaptive VST as a preprocessing stage for four existing computerized MC detection methods on three data sets acquired with mammographic units from different manufacturers. In all the test cases considered, MC detection performance on transformed mammograms was statistically significantly higher than on unprocessed mammograms. Results were also superior in comparison with a “fixed” (nonparametric) VST previously proposed for digital mammograms.

Improving the Automated Detection of Calcifications Using Adaptive Variance Stabilization

Traditionally, the High-Level Synthesis (HLS) for Field Programmable Gate Array (FPGA) devices is a methodology that transforms a behavioral description, as the timing-independent specification, to an abstraction level that is synthesizable, like the Register Transfer Level This process can be performed under a framework that is known as Design Space Exploration (DSE), which helps to determine the best design by addressing scheduling, allocation, and binding problems, all three of which are NP-hard problems In this manner, and due to the increased complexity of modern digital circuit designs and concerns regarding the capacity of the FPGAs, designers are proposing novel HLS techniques capable of performing automatic optimization HLS has several conflicting metrics or objective functions, such as delay, area, power, wire length, digital noise, reliability, and security For this reason, it is suitable to apply Multiobjective Optimization Algorithms (MOAs), which can handle the different trade-offs among the objective functions During the last two decades, several MOAs have been applied to solve this problem This paper introduces a comprehensive analysis of different MOAs that are suitable to perform HLS for FPGA devices We highlight significant aspects of MOAs, namely, optimization methods, intermediate structures where the optimizations are performed, HLS techniques that are addressed, and benchmarks and performance assessments employed for experimentation In addition, we show the analysis of how multiple objectives are optimized currently in the algorithms and which are the objective functions that are optimized Finally, we provide insights and suggestions to contribute to the solution of major research challenges in this area

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Development of Multiobjective High-Level Synthesis for FPGAs

A breast tissue type detection system is designed, and verified on a publicly available mammogram dataset constructed by the Mammographic Image Analysis Society (MIAS) in this paper. This database consists of three fundamental breast tissue types that are fatty, fatty-glandular, and dense-glandular. At the pre-processing stage of the designed detection system, median filtering and morphological operations are applied for noise reduction and artifact suppression, respectively; then a pectoral muscle removal operation follows by using a region growing algorithm. Then, 88-dimensional texture features are computed from the GLCMs (Gray-Level Co-Occurrence Matrices) of mammogram images. Besides, a formerly introduced 108-dimensional feature ensemble is also computed and cascaded with the 88-dimensional texture features. Finally, a classification process is realized using Fisher’s Linear Discriminant Analysis (FLDA) classifier in four different classification cases: one-stage classification, first fatty – then others, first fatty-glandular – then others, and first dense-glandular – then others. A maximum of 72.93% classification accuracy is achieved using only texture features whereas it is increased to 82.48% when cascade features are utilized. This consequence clearly exposes that the cascade features are more representative than texture features. The maximum classification accuracy is attained when “first fatty-glandular – then others” classification case is implemented, that is consistent with the fact that fatty-glandular tissue type is easily confused with fatty and dense-glandular tissue types.

https://www.ijisae.org/IJISAE/article/download/945/522

A Genuine GLCM-based Feature Extraction for Breast Tissue Classification on Mammograms

We present here an experimental work on mammogram denoising by the mathematical tool called the curvelet transform. The infiltration of noise in mammogram during the X-ray screening is a common and inevitable phenomenon. And such noise is normally reduced by the curvelet transform based on conventional thresholding strategy called the hard thresholding (HT). Therefore, the motive of this experimentation is to suggest an alternate but efficient mechanism of mammogram denoising by the same transform but with different algorithms purely based on the information of the neighbouring coefficients. It is found that the curvelet transform applied with the precept of surrounding curvelet coefficients is visually and statistically better than the conventional approach based on HT.

Mammogram denoising by curvelet transform based on the information of neighbouring coefficients

We present an experimental work on the denoising of mammogram with Poisson noise. Reviewing the literature, it is found that the denoising performance of the multiresolution tools like wavelet, contourlet and curvelet implemented on mammogram with Poisson noise is unique. The first part of the investigation deals with the confirmation of this exceptional performance with our result. The later half implements the recently developed denoising approach called the Poisson Unbiased Risk Estimation-Linear Expansion of Thresholds (PURE-LET) to the Poisson noise corrupted mammogram with an objective to improve the peak signal to noise ratio (PSNR) further. The PURE-LET successfully removes Poisson noise better than the traditional mathematical transforms already mentioned. The computation time and PSNR are also evaluated in the perspective of the cycle spinning technique. This validates the applicability and efficiency of the novel denoising strategy in the field of digital mammography.

Poisson Noise Removal from Mammogram Using Poisson Unbiased Risk Estimation Technique

High Level Synthesis (HLS) for application-specific computer hardware design involves critical decision making steps such as design space exploration (DSE) of resources, scheduling, allocation and binding. Exploring an optimal design solution comprising of array of resource types is a complex problem and requires intelligent decision making in an automated manner. Exploration of an optimal design solution is considered a complex problem as it involves trade-off between contradictory parameters of hardware area and latency amongst numerous candidate design variants. This paper presents the following contributions: (a) novel FA driven DSE methodology during high level synthesis for application specific computing hardware based on area-latency trade-off (b) novel sensitivity analysis that provides optimal tuning of FA control parameters for performing DSE that leads to faster convergence.

A Firefly Algorithm Driven Approach for High Level Synthesis

A DSP circuit is considered to be secure, if its functionality is designed to be hidden from an adversary. In other words to make a DSP design secured, its hardware architecture should not look obvious in terms of its functionality. Structural obfuscation plays a critical role in realizing this objective. In the context of transient fault secured DSP circuits, one of the popular design approaches is using dual modular redundancy (DMR). This established design practice makes the functionality of common fault secured DSP circuits architecture easily identifiable to an adversary. In this paper we propose a novel obfuscation in the context of fault secure DSP circuit that uses hybrid transformations in successive layers to completely transform the hardware architecture of the design at register transfer (RT) and gate level without disturbing its functionality and incurring any design overhead. Results indicate without incurring any design cost overhead, the proposed obfuscation achieves significant structural transformation at the gate level such that the functionality becomes un-obvious to an adversary.

Obfuscation of Fault Secured DSP Design Through Hybrid Transformation

This paper deals with the detection of a genetic disorder called Darier disease manifested in dermal changes. The clinical diagnosis of the said disease motivates its recognition in the light of computer vision which incorporates three methodologies based on 1) gray level co-occurrence matrix (GLCM), 2) local binary pattern (LBP) and 3) wavelet energy feature for skin texture feature extraction. The feed forward neural network (FNN) is implemented for Darier disease detection. All the methodologies are thoroughly compared to find the most suitable one as skin texture screening tool. The GLCM, LBP and wavelet based methodologies attain Darier disease detection accuracy of about 82%, 82% and 89% respectively. The other aspect of this work is that GLCM based methodology addresses the presence and location of several typical skin texture abnormalities by the statistical plots drawn against user defined offsets (spatial relationship between two pixels).

Mrinal Kanti Naskar

Papers

Mammogram denoising by curvelet transform based on the information of neighbouring coefficients

Poisson Noise Removal from Mammogram Using Poisson Unbiased Risk Estimation Technique

A Firefly Algorithm Driven Approach for High Level Synthesis

Obfuscation of Fault Secured DSP Design Through Hybrid Transformation

Darier Disease-A Genetic Disorder Detection in the Light of Computer Vision