Saro Meguerdichian

Gate-level characterization: foundations and hardware security applications

Gate-level characterization (GLC) is the process of characterizing each gate of an integrated circuit (IC) in terms of its physical and manifestation properties. It is a key step in the IC applications regarding cryptography, security, and digital rights management. However, GLC is challenging due to the existence of manufacturing variability (MV) and the strong correlations among some gates in the circuit. We propose a new solution for GLC by using thermal conditioning techniques. In particular, we apply thermal control on the process of GLC, which breaks the correlations by imposing extra variations concerning gate level leakage power. The scaling factors of all the gates can be characterized by solving a system of linear equations using linear programming (LP). Based on the obtained gate level scaling factors, we demonstrate an application of GLC, hardware Trojan horse (HTH) detection, by using constraint manipulation. We evaluate our approach of GLC and HTH detection on several ISCAS85/89 benchmarks. The simulation results show that our thermally conditioned GLC approach is capable of characterizing all the gates with an average error less than the measurement error, and we can detect HTHs with 100% accuracy on a target circuit.

Device aging-based physically unclonable functions

To improve resiliency against reverse engineering we propose dynamic physically unclonable functions (DPUFs) whose physical properties are subject to unpredictable changes between uses. We demonstrate this idea using device aging to alter delay characteristics according to user instructions.

Malicious Circuitry Detection Using Thermal Conditioning

Gate-level characterization (GLC) is the process of quantifying physical and manifestational properties for each gate of an integrated circuit (IC). It is a key step in many IC applications that target cryptography, security, digital rights management, low power, and yield optimization. However, GLC is a challenging task due to the size and structure of modern circuits and insufficient controllability of a subset of gates in the circuit. We have developed a new approach for GLC that employs thermal conditioning to calculate the scaling factors of all the gates by solving a system of linear equations using linear programming (LP). Therefore, the procedure captures the complete impact of process variation (PV). In order to resolve the correlations in the system of linear equations, we expose different gates to different temperatures and thus change their corresponding linear coefficients in the linear equations. We further improve the accuracy of GLC by applying statistical methods in the LP formulation as well as the post-processing steps. In order to enable non-destructive hardware Trojan horse (HTH) detection, we generalize our generic GLC procedure by manipulating the constraint of each linear equation. Furthermore, we ensure the scalability of the approaches for GLC and HTH detection using iterative IC segmentation. We evaluate our approach on a set of ISCAS and ITC benchmarks.

Matched public PUF: ultra low energy security platform

Hardware-based physically unclonable functions (PUFs) leverage intrinsic process variation of modern integrated circuits to provide interesting security solutions but either induce high storage requirements or require significant resources of at least one involved party. We use device aging to realize two identical unclonable modules that cannot be matched with any third such module. Each device enables rapid, low-energy computation of ultra-complex functions that are too complex for simulation in any reasonable time. The approach induces negligible area and energy costs and enables a majority of security protocols to be completed in a single or a few clock cycles.

Trusted sensors and remote sensing

Remote trusted operation is essential for many types of sensors in an even greater number of applications. It is often crucial to secure guarantees that a particular sensor sample is taken by a specific sensor at a particular time and stated location. We present the first generic system architecture and security protocol that provides low cost, low power, and low latency trusted remote sensing. The approach employs already known randomized challenges and public physically unclonable function with a new concept of interleaved operational and security circuitry.

Security primitives and protocols for ultra low power sensor systems

Security requirements in sensor systems include resiliency against physical and side-channel attacks, low energy for communication, storage, and computation, and the ability to realize a variety of public-key protocols. Furthermore, primitives and protocols that enable trusted remote operation in terms of data, time, and location are essential to guarantee secure sensing. By integrating physically unclonable functions (PUFs) directly into sensor hardware and using device aging to securely match groups of sensors, we enable a variety of ultra low power security protocols for trusted remote sensing, including authentication and public key communication.

Energy optimization in wireless medical systems using physiological behavior

Wearable sensing systems are becoming widely used for a variety of applications, including sports, entertainment, and military. These systems have recently enabled a variety of medical monitoring and diagnostic applications in Wireless Health. The need for multiple sensors and constant monitoring lead these systems to be power hungry and expensive, with short operating lifetimes. In this paper, we introduce a novel methodology that takes advantage of the influence of human behavior on signal properties and reduces those three metrics from the data size point of view. This, in turn, directly influences the wireless communication and local processing power consumption. We exploit intrinsic space and temporal correlations between sensor data while considering both user and system behavior. Our goal is to select a small subset of sensors to accurately capture and/or predict all possible signals of a fully instrumented wearable sensing system. Our approach leverages novel modeling, partitioning, and behavioral optimization, which consists of signal characterization, segmentation and time shifting, mutual signal prediction, and subset sensor selection. We demonstrate the effectiveness of the technique on an insole instrumented with 99 pressure sensors placed in each shoe, which cover the bottom of the entire foot, resulting in energy reduction of 56% to 96% for error rates of 5% to 17.5%.

Using standardized quantization for multi-party PPUF matching: foundations and applications

Hardware-based physically unclonable functions (PUFs) are elegant security primitives that leverage process variation inherent in modern integrated circuits. Recently proposed matched public PUFs (mPPUFs) use a combination of coordinated device aging and gate disabling to create two PUFs that securely realize identical input-output mappings. However, mPPUFs of any reasonable size allow for protocols between only a very limited number of parties. We propose quantization of possible delay values to enable matching of an unbounded number of arbitrary PPUF instances, improving stability in the presence of fluctuations in temperature or supply voltage while maintaining resiliency against a wide number of attacks.

A gate level sensor network for integrated circuits temperature monitoring

We present the first sensor network architecture to monitor integrated circuits (IC) thermal and energy activity. The sensor network consists of a set of simple gates, which are superimposed over the actual design of any IC. The sensing network and the actual IC design are completely disjoint in order to enable their simultaneous operation. Since the delay of gates is proportional to their temperature, we can obtain temperature of the network gates, by measuring the delay of the gates in the self-sensing network. Once we measured the delay of the circuit, we use CMOS temperature-delay relation and linear programming formulation to calculate the temperature at any point on the chip. High resolution (spatial and temporal) temperature monitoring allows several run-time optimizations. Protecting shared processors from permanent localized damage through rapid creation of hot spots and efficient accounting of the available energy supply are among two main applications of our IC sensor network.

Semantic multimodal compression for wearable sensing systems

Wearable sensing systems (WSSs) are emerging as an important class of distributed embedded systems in application domains ranging from medical to military. Such systems can be expensive and power hungry due to their multi-sensor implementations that require constant use, yet by nature they demand low-cost and low-power implementations. Semantic multimodal compression (SMC) mitigates these metrics in terms of data size by leveraging the natural tendency of signals in many types of embedded sensing systems to be composed of phases. In our driving example of a medical shoe with an insole lined with pressure sensors, we find that the natural airborne, landing, and take-off segments have sharply different and repetitive properties. SMC models and compresses each segment independently, selecting the best compression scheme for each segment and thus reducing total transmission energy.