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A Trojan attack maliciously modifies, alters, or embeds unplanned components inside the exploited chips. Given the original chip specifications, and process and simulation models, the goal of Trojan detection is to identify the malicious components. This paper introduces a new Trojan detection method based on nonintrusive external IC quiescent current measurements. We define a new metric called consistency. Based on the consistency metric and properties of the objective function, we present a robust estimation method that estimates the gate properties while simultaneously detecting the Trojans. Experimental evaluations on standard benchmark designs show the validity of the metric, and demonstrate the effectiveness of the new Trojan detection.

Convolutional Neural Networks (CNN) have been the centerpiece of many applications including but not limited to computer vision, speech processing, and Natural Language Processing (NLP). However, the computationally expensive convolution operations impose many challenges to the performance and scalability of CNNs. In parallel, photonic systems, which are traditionally employed for data communication, have enjoyed recent popularity for data processing due to their high bandwidth, low power consumption, and reconfigurability. Here we propose a Photonic Convolutional Neural Network Accelerator (PCNNA) as a proof of concept design to speedup the convolution operation for CNNs. Our design is based on the recently introduced silicon photonic microring weight banks, which use broadcast-and-weight protocol to perform Multiply And Accumulate (MAC) operation and move data through layers of a neural network. Here, we aim to exploit the synergy between the inherent parallelism of photonics in the form of Wavelength Division Multiplexing (WDM) and sparsity of connections between input feature maps and kernels in CNNs. While our full system design offers up to more than 3 orders of magnitude speedup in execution time, its optical core potentially offer more than 5 order of magnitude speedup compared to state-of-the-art electronic counterparts.

Neural Networks (NNs) have become the mainstream technology in the artificial intelligence (AI) renaissance over the past decade. Among different types of neural networks, convolutional neural networks (CNNs) have been widely adopted as they have achieved leading results in many fields such as computer vision and speech recognition. This success in part is due to the widespread availability of capable underlying hardware platforms. Applications have always been a driving factor for design of such hardware architectures. Hardware specialization can expose us to a novel architectural solutions, which can outperform general purpose computers for tasks at hand. Although different applications demand for different performance measures, they all share speed and energy efficiency as high priorities. Meanwhile, photonics processing has seen a resurgence due to its inherited high speed and low power nature.Here, we investigate the potential of using photonics in CNNs by proposing a CNN accelerator design based on Winograd filtering algorithm. Our evaluation results show that while a photonic accelerator can compete with current-state-of-the-art electronic platforms in terms of both speed and power, it has the potential to improve the energy efficiency by up to three orders of magnitude.

We present the first approach for post-silicon leakage power reduction through input vector control (IVC) that takes into account the impact of the manufacturing variability (MV). Because of the MV, the integrated circuits (ICs) implementing one design require different input vectors to achieve their lowest leakage states. We address two major challenges. The first is the extraction of the gatelevel characteristics of an IC by measuring only the overall leakage power for different inputs. The second problem is the rapid generation of input vectors that result in a low leakage for a large number of unique ICs that implement a given design, but are different in the post-manufacturing phase. Experimental results on a large set of benchmark instances demonstrate the efficiency of the proposed methods. For example, the leakage power consumption could be reduced in average by more than 10.4%, when compared to the previously published IVC techniques that did not consider MV.