Modern society is striving for digital connectivity that demands information security. As an emerging technology, printed electronics is a key enabler for novel device types with free form factors, customizability, and the potential for large-area fabrication while being seamlessly integrated into our everyday environment. At present, information security is mainly based on software algorithms that use pseudo random numbers. In this regard, hardware-intrinsic security primitives, such as physical unclonable functions, are very promising to provide inherent security features comparable to biometrical data. Device-specific, random intrinsic variations are exploited to generate unique secure identifiers. Here, we introduce a hybrid physical unclonable function, combining silicon and printed electronics technologies, based on metal oxide thin film devices. Our system exploits the inherent randomness of printed materials due to surface roughness, film morphology and the resulting electrical characteristics. The security primitive provides high intrinsic variation, is non-volatile, scalable and exhibits nearly ideal uniqueness.

As FPGAs are used in cloud applications they reveal new challenges in cloud security, in which the FPGA-mapped design can compromise the security of other FPGA tenants. In this work, we show that programmable resources of an FPGA can be utilized to perform side channel attacks. More precisely, we develop internal sensors which are based on programmable primitives, and transfer the internally-measured side-channel leakages outside. These are distributed and calibrated delay sensors which can indirectly measure voltage fluctuations due to power consumption. By means of a cryptographic core as a case study, we present different settings and parameters for our employed sensors. Using their side-channel measurements, we further exhibit practical keyrecovery attacks confirming the applicability of the underlying measurement methodology. This opens a new door to integrate hardware Trojans in a) applications where the FPGA is remotely accessible, such as in the cloud and b) multi-tenant FPGAs, where the reconfigurable resources are shared among different users. This type of attack is highly difficult to detect, since there is no signal connection between targeted (cryptographic) core and the internally-deployed sensors.

Memories occupy most of the silicon area in nowadays' system-on-chips and contribute to a significant part of system power consumption. Though widely used, nonvolatile Flash memories still suffer from several drawbacks. Magnetic random access memories (MRAMs) have the potential to mitigate most of the Flash shortcomings. Moreover, it is predicted that they could be used for DRAM and SRAM replacement. However, they are prone to manufacturing defects and runtime failures as any other type of memory. This article provides an up-to-date and practical coverage of MRAM test and reliability solutions existing in the literature. After some background on existing MRAM technologies, defectiveness and reliability issues are discussed, as well as functional fault models used for MRAM. This article is dedicated to a summarized description of existing test and reliability improvement methods developed so far for various MRAM technologies. The last part of this article gives some perspectives on this hot topic.

Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that feature ultra-low cost, conformity, and non-toxicity requirements that silicon-based systems cannot deliver. Nevertheless, large feature sizes in PE prohibit the realization of complex printed ML circuits. In this work, we present, for the first time, an automated printed-aware software/hardware co-design framework that exploits approximate computing principles to enable ultra-resource constrained printed multilayer perceptrons (MLPs). Our evaluation demonstrates that, compared to the state-of-the-art baseline, our circuits feature on average 6x (5.7x) lower area (power) and less than 1% accuracy loss.

Multi-tenant FPGAs, in which 3rd parties have partial access to the FPGA fabric, are a rising usage trend in cloud and reconfigurable SoCs. This gives rise to new types of attacks in FPGAs, as shown in recent studies. These attacks can operate on the electrical level through the common power delivery network, making them very hard to isolate. Thus, software-controlled FPGA configuration can be exploited to insert hardware trojans, impacting the security of the entire system. The attacks can be separated into fault and side-channel attacks to either actively manipulate a system or quietly extract secret information. In this paper, we show the first attempt of countermeasures against these voltage fluctuation based attacks, by analyzing FPGA bitstreams for malicious logic, basically implementing an FPGA antivirus. We provide a way to check bitstreams for potentially malicious structures, by extending a combination of commercial and open-source tools.

FPGAs are being offered in the cloud as accelerator resources that can be shared among multiple users (i.e. tenants). Recently, various approaches have shown that fault attacks launched from one tenant region to another are possible, leading to timing faults or crashes of the FPGA. It is, therefore, important that malicious tenants are limited in their ability to cause such security problems. So far, the existing countermeasures against such attacks check the configuration bitstreams before they are reconfigured. Such offline approaches have various practical limitations, e.g. they may force the tenants to unveil their design secrets. In this paper, we present LoopBreaker, a novel runtime solution that can disable the entire activity of a malicious tenant region, in order to rapidly stop a potential attack before it results in a crash (i.e. Denial-of-Service). We implemented and tested multiple attack types and found that realistic attacks demand at least 12–26 µs to be successful. A partial reconfiguration to overwrite the malicious tenant region demands 200 µs in our realworld implementation, which is too slow to prevent the attack from leading to a crash. Instead, our proposed LoopBreaker method only needs 1.5 µs to stop a malicious tenant, which makes it the first online approach that can successfully stop challenging voltage drop-based attacks from causing a crash.