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In the past, epidemics such as AIDS, measles, SARS, H1N1 influenza, and tuberculosis caused the death of millions of people around the world. In response, intensive research is evolving to design efficient drugs and vaccines. However, studies warn that new pandemics such as Coronavirus (COVID-19), variants, and even deadly pandemics can emerge in the future. The existing epidemic confinement approaches rely on a large amount of available data to determine policies. Such dependencies could cause an irreversible effect before proper strategies are developed. Furthermore, the existing approaches follow a one-size-fits-all control technique, which might not be effective. To overcome this, in this work, we develop a game-theory-inspired approach that considers societal and economic impacts and formulates epidemic control as a non-zero-sum game. Further, the proposed approach considers the demographic information that provides a tailored solution to each demography. We explore different strategies, including masking, social distancing, contact tracing, quarantining, partial-, and full-lockdowns and their combinations, and present demography-aware optimal solutions to confine a pandemic with minimal history information and optimal impact on the economy. To facilitate scalability, we propose a novel graph learning approach, which learns from the previously obtained COVID-19 game outputs and mobility rates of one state (region)more »depending on the other to produce an optimal solution. Our optimal solution is strategized to restrict the mobility between states based on the impact they are causing on COVID-19 spread. We aim to control the COVID-19 spread by more than 50% and model a dynamic solution that can be applied to different strains of COVID-19. Real-world demographic conditions specific to each state are created, and an optimal strategic solution is obtained to reduce the infection rate in each state by more than 50%.« less

Modern semiconductor manufacturing often leverages a fabless model in which design and fabrication are partitioned. This has led to a large body of work attempting to secure designs sent to an untrusted third party through obfuscation methods. On the other hand, efficient de-obfuscation attacks have been proposed, such as Boolean Satisfiability attacks (SAT attacks). However, there is a lack of frameworks to validate the security and functionality of obfuscated designs. Additionally, unconventional obfuscated design flows, which vary from one obfuscation to another, have been key impending factors in realizing logic locking as a mainstream approach for securing designs. In this work, we address these two issues for Lookup Table-based obfuscation. We study both Volatile and Non-volatile versions of LUT-based obfuscation and develop a framework to validate SAT runtime using machine learning. We can achieve unparallel SAT-resiliency using LUT-based obfuscation while incurring 7% area and less than 1% power overheads. Following this, we discuss and implement a validation flow for obfuscated designs. We then fabricate a chip consisting of several benchmark designs and a RISC-V CPU in TSMC 65nm for post functionality validation. We show that the design flow and SAT-runtime validation can easily integrate LUT-based obfuscation into existing CAD toolsmore »while adding minimal verification overhead. Finally, we justify SAT-resilient LUT-based obfuscation as a promising candidate for securing designs.« less

This paper presents RAPTA, a customized Representation-learning Architecture for automation of feature engineering and predicting the result of Path-based Timing-Analysis early in the physical design cycle. RAPTA offers multiple advantages compared to prior work: 1) It has superior accuracy with errors std ranges 3.9ps~16.05ps in 32nm technology. 2) RAPTA's architecture does not change with feature-set size, 3) RAPTA does not require manual input feature engineering. To the best of our knowledge, this is the first work, in which Bidirectional Long Short-Term Memory (Bi-LSTM) representation learning is used to digest raw information for feature engineering, where generation of latent features and Multilayer Perceptron (MLP) based regression for timing prediction can be trained end-to-end.

Maximizing profits while minimizing risk in a technologically advanced silicon industry has motivated the globalization of the fabrication process and electronic hardware supply chain. However, with the increasing magnitude of successful hardware attacks, the security of many hardware IPs has been compromised. Many existing security works have focused on resolving a single vulnerability while neglecting other threats. This motivated to propose a novel approach for securing hardware IPs during the fabrication process and supply chain via logic obfuscation by utilizing emerging spin-based devices. Our proposed dynamic obfuscation approach uses reconfigurable logic and interconnects blocks (RIL-Blocks), consisting of Magnetic Random Access Memory (MRAM)-based Look Up Tables and switch boxes flexibility and resiliency against state-of-the-art SAT-based attacks and power side-channel attacks while incurring a small overhead. The proposed Scan Enabled Obfuscation circuitry obfuscates the oracle circuit’s responses and further fortifies the logic and routing obfuscation provided by the RIL-Blocks, resembling a defense-in-depth approach. The empirical evaluation of security provided by the proposed RIL-Blocks on the ISCAS benchmark and common evaluation platform (CEP) circuit shows that resiliency comes with reduced overhead while providing resiliency to various hardware security threats.

To enable trust in the IC supply chain, logic locking as an IP protection technique received significant attention in recent years. Over the years, by utilizing Boolean satisfiability (SAT) solver and its derivations, many de-obfuscation attacks have undermined the security of logic locking. Nonetheless, all these attacks receive the inputs (locked circuits) in a very simplified format (Bench or remapped and translated Verilog) with many limitations. This raises the bar for the usage of the existing attacks for modeling and assessing new logic locking techniques, forcing the designers to undergo many troublesome translations and simplifications. This paper introduces the RANE Attack, an open-source CAD-based toolbox for evaluating the security of logic locking mechanisms that implement a unique interface to use formal verification tools without a need for any translation or simplification. The RANE attack not only performs better compared to the existing de-obfuscation attacks, but it can also receive the library-dependent logic-locked circuits with no limitation in written, elaborated, or synthesized standard HDL, such as Verilog. We evaluated the capability/performance of RANE on FOUR case studies, one is the first de-obfuscation attack model on FSM locking solutions (e.g., HARPOON) in which the key is not a static bit-vector but amore »sequence of input patterns.« less

In the last decades, emerging and re-emerging epidemics such as AIDS, measles, SARS, HINI influenza, and tuberculosis cause death to millions of people each year. In response, a large and intensive research is evolving for the design of better drugs and vaccines. However, studies warn that the new pandemics such as Coronavirus (COVID-19) and even deadly pandemics can emerge in the future. The existing confinement approaches rely on large amount of available data to determine policies. Such dependencies could cause an irreversible effect before proper strategies are developed. Furthermore, the existing approaches follow a one-size fits all approach, which might not be effective. In contrast, we develop a game-theory inspired approach that considers societal and economic impacts and formulates the epidemic control as a non-zero sum dynamic game. Further, the proposed approach considers the demographic information leading to providing a tailored solution to each demography. We explore different strategies including masking, social distancing, contact tracing, quarantining, partial-, and full-lockdowns and their combinations and present demography-aware optimal solutions to confine a pandemic with minimal history information and optimal impact on economy.

Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational complexity is growing exponentially. In this paper, we propose a novel solution to reduce the computational complexity of convolutional neural network models used for many class image classification. Our proposed technique breaks the classification task into two steps: 1) coarse-grain classification, in which the input samples are classified among a set of hyper-classes, 2) fine-grain classification, in which the final labels are predicted among those hyper-classes detected at the first step. We illustrate that our proposed classifier can reach the level of accuracy reported by the best in class classification models with less computational complexity (Flop Count) by only activating parts of the model that are needed for the image classification.