367 research outputs found
Real-time Analog Pixel-to-pixel Dynamic Frame Differencing with Memristive Sensing Circuits
In this paper, we propose an analog pixel differencing circuit for
differentiating pixels between frames directly from CMOS pixels. The analog
information processing at sensor is a topic of growing appeal to develop edge
AI devices. The proposed circuit is integrated into a pixel-parallel and
pixel-column architectures. The proposed system is design using TSMC
CMOS technology. The power dissipation of the proposed circuit is ,
and on-chip ares is . The architectures are tested for moving
object detection application.Comment: IEEE SENSORS 201
Impact of laser attacks on the switching behavior of RRAM devices
The ubiquitous use of critical and private data in electronic format requires reliable and secure embedded systems for IoT devices. In this context, RRAMs (Resistive Random Access
Memories) arises as a promising alternative to replace current memory technologies. However,
their suitability for this kind of application, where the integrity of the data is crucial, is still under
study. Among the different typology of attacks to recover information of secret data, laser attack
is one of the most common due to its simplicity. Some preliminary works have already addressed
the influence of laser tests on RRAM devices. Nevertheless, the results are not conclusive since
different responses have been reported depending on the circuit under testing and the features of
the test. In this paper, we have conducted laser tests on individual RRAM devices. For the set of
experiments conducted, the devices did not show faulty behaviors. These results contribute to the
characterization of RRAMs and, together with the rest of related works, are expected to pave the way for the development of suitable countermeasures against external attacks.Postprint (published version
A PUF based Lightweight Hardware Security Architecture for IoT
With an increasing number of hand-held electronics, gadgets, and other smart devices, data is present in a large number of platforms, thereby increasing the risk of security, privacy, and safety breach than ever before. Due to the extreme lightweight nature of these devices, commonly referred to as IoT or `Internet of Things\u27, providing any kind of security is prohibitive due to high overhead associated with any traditional and mathematically robust cryptographic techniques. Therefore, researchers have searched for alternative intuitive solutions for such devices. Hardware security, unlike traditional cryptography, can provide unique device-specific security solutions with little overhead, address vulnerability in hardware and, therefore, are attractive in this domain. As Moore\u27s law is almost at its end, different emerging devices are being explored more by researchers as they present opportunities to build better application-specific devices along with their challenges compared to CMOS technology. In this work, we have proposed emerging nanotechnology-based hardware security as a security solution for resource constrained IoT domain. Specifically, we have built two hardware security primitives i.e. physical unclonable function (PUF) and true random number generator (TRNG) and used these components as part of a security protocol proposed in this work as well. Both PUF and TRNG are built from metal-oxide memristors, an emerging nanoscale device and are generally lightweight compared to their CMOS counterparts in terms of area, power, and delay. Design challenges associated with designing these hardware security primitives and with memristive devices are properly addressed. Finally, a complete security protocol is proposed where all of these different pieces come together to provide a practical, robust, and device-specific security for resource-limited IoT systems
An overview of memristive cryptography
Smaller, smarter and faster edge devices in the Internet of things era
demands secure data analysis and transmission under resource constraints of
hardware architecture. Lightweight cryptography on edge hardware is an emerging
topic that is essential to ensure data security in near-sensor computing
systems such as mobiles, drones, smart cameras, and wearables. In this article,
the current state of memristive cryptography is placed in the context of
lightweight hardware cryptography. The paper provides a brief overview of the
traditional hardware lightweight cryptography and cryptanalysis approaches. The
contrast for memristive cryptography with respect to traditional approaches is
evident through this article, and need to develop a more concrete approach to
developing memristive cryptanalysis to test memristive cryptographic approaches
is highlighted.Comment: European Physical Journal: Special Topics, Special Issue on
"Memristor-based systems: Nonlinearity, dynamics and applicatio
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