Stochastic Memory Devices for Security and Computing

With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware-based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large-area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive-switching memory (RRAM), phase-change memory (PCM), and spin-transfer torque magnetic memory (STT-MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed

Emerging physical unclonable functions with nanotechnology

Physical unclonable functions (PUFs) are increasingly used for authentication and identification applications as well as the cryptographic key generation. An important feature of a PUF is the reliance on minute random variations in the fabricated hardware to derive a trusted random key. Currently, most PUF designs focus on exploiting process variations intrinsic to the CMOS technology. In recent years, progress in emerging nanoelectronic devices has demonstrated an increase in variation as a consequence of scaling down to the nanoregion. To date, emerging PUFs with nanotechnology have not been fully established, but they are expected to emerge. Initial research in this area aims to provide security primitives for emerging integrated circuits with nanotechnology. In this paper, we review emerging nanotechnology-based PUFs

Probabilistic design for emerging memory and nanometer-scale logic

As semiconductor technology has scaled down, the impact of stochastic behavior in very large scale integrated circuits (VLSI) has become an ever-more important concern. This dissertation investigates two distinct classes of problems that require the use of probabilistic methods and models: (1) Modeling and exploiting stochastic behavior in advanced memory technologies; (2) Probabilistic modeling of faults due to on-chip voltage variation. This dissertation first investigates the unique physics-level stochasticity of spin-transfer torque magnetic RAM (STT-RAM). The write process of STT-RAM is stochastic: specifically, the write time of a bitcell varies significantly. The wors-tcase approach, which uses the longest write pulse duration, guarantees a successful write; however, it introduces significant energy overhead due to excessive margins since the average write pulse duration is far shorter than the worst-case pulse duration. This dissertation develops novel circuit techniques to exploit the stochastic properties of STT-RAM write operation for energy savings by moving away from the worst-case approach to dynamic strategies while maintaining the required low error rate. The first contribution is a variable energy write (VEW) architecture that effectively exploits the wide distribution of write time to greatly reduce energy via a mechanism that checks the instantaneous state of the bitcell and deactivates the write current once the correct value has registered. The second contribution is a multiple attempt write (MAW) strategy that utilizes the asymptotic temporal stochastic independence of repeated switching events to achieve a dramatic reduction in energy. The proposed architectures are evaluated using a compact STT-RAM cell model. Analysis indicates that VEW succeeded in reducing the write energy by 94.7% with approximately 1% relative area overhead under an efficient design methodology compared with the conventional designs relying on the worst case approach. MAW reduced the overall write energy by 94.6% with approximately 0.05% relative area overhead. This dissertation then addresses the problem of probabilistic modeling of faults due to on-chip voltage variations. The power supply voltage variation can increase gate delay, resulting in timing faults on near-critical paths. These low-level faults ultimately propagate to architecture and application levels, often leading to critical system failures. Developing an accurate fault model and injection tool that generates and propagates faults from circuit- to gate-level is important for accurately predicting the resulting system failures. This is challenging since the model needs to accurately capture the physical characteristics at the circuit level that define the likelihood of a fault and use that information to guide the injection with the proper probability. At the same time, the analysis and fault injections need to be computationally manageable to allow analysis of realistic systems under realistic workloads. The conventional fault models rely on either Monte Carlo sampling or time-consuming runtime simulation using the worst-case voltage drop. To overcome simulation overheads of runtime circuit-level simulation, a novel two-phase approach is proposed. The main idea is that circuit characterization can be done before simulation. The result of pre-characterization is used at runtime via a form of look-up to enable gate-level efficiency. The two-phase methodology is time-efficient but may require high memory unless the look-up tables are carefully optimized. This dissertation also develops the fault probability estimation based on workload-specific voltage distribution, rather than a fixed worst-case voltage. The proposed methodology is implemented on an OpenSPARC design targeting on a 32nm technology node. Analysis indicates the proposed fault modeling and injection flow reduces runtime overhead by 24X compared to the previously best-known gate-level fault simulator while having circuit level accuracy.Electrical and Computer Engineerin

Embracing Low-Power Systems with Improvement in Security and Energy-Efficiency

As the economies around the world are aligning more towards usage of computing systems, the global energy demand for computing is increasing rapidly. Additionally, the boom in AI based applications and services has already invited the pervasion of specialized computing hardware architectures for AI (accelerators). A big chunk of research in the industry and academia is being focused on providing energy efficiency to all kinds of power hungry computing architectures. This dissertation adds to these efforts. Aggressive voltage underscaling of chips is one the effective low power paradigms of providing energy efficiency. This dissertation identifies and deals with the reliability and performance problems associated with this paradigm and innovates novel energy efficient approaches. Specifically, the properties of a low power security primitive have been improved and, higher performance has been unlocked in an AI accelerator (Google TPU) in an aggressively voltage underscaled environment. And, novel power saving opportunities have been unlocked by characterizing the usage pattern of a baseline TPU with rigorous mathematical analysis

Model Building and Security Analysis of PUF-Based Authentication

In the context of hardware systems, authentication refers to the process of confirming the identity and authenticity of chip, board and system components such as RFID tags, smart cards and remote sensors. The ability of physical unclonable functions (PUF) to provide bitstrings unique to each component can be leveraged as an authentication mechanism to detect tamper, impersonation and substitution of such components. However, authentication requires a strong PUF, i.e., one capable of producing a large, unique set of bits per device, and, unlike secret key generation for encryption, has additional challenges that relate to machine learning attacks, protocol attacks and constraints on device resources. We describe the requirements for PUF-based authentication, and present a PUF primitive and protocol designed for authentication in resource constrained devices. Our experimental results are derived from a 28 nm Xilinx FPGA. In the authentication scenario, strong PUFs are required since the adversary could collect a subset of challenges and response pairsto build a model and predict the responses for unseen challenges. Therefore, strong PUFs need to provide exponentially large challenge space and be resilient to model building attacks. We investigate the security properties of a Hardware-embedded Delay PUF called HELP which leverages within-die variations in path delays within a hardware-implemented macro (functional unit) as the entropy source. Several features of the HELP processing engine significantly improve its resistance to model-building attacks. We also investigate a novel technique that significantly improves the statistically quality of the generated bitstring for HELP. Stability across environmental variations such as temperature and voltage, is critically important for Physically Unclonable Functions (PUFs). Nearly all existing PUF systems to date need a mechanism to deal with “bit flips” when exact regeneration of the bitstring is required, e.g., for cryptographic applications. Error correction (ECC) and error avoidance schemes have been proposed but both of these require helper data to be stored for the regeneration process. Unfortunately, helper data adds time and area overhead to the PUF system and provides opportunities for adversaries to reverse engineer the secret bitstring. We propose a non-volatile memory-based (NVM) PUF that is able to avoid bit flips without requiring any type of helper data. We describe the technique in the context of emerging nano-devices, in particular, resistive random access memory (Memristor) cells, but the methodology is applicable to any type of NVM including Flash