A Review of Bayesian Methods in Electronic Design Automation

The utilization of Bayesian methods has been widely acknowledged as a viable solution for tackling various challenges in electronic integrated circuit (IC) design under stochastic process variation, including circuit performance modeling, yield/failure rate estimation, and circuit optimization. As the post-Moore era brings about new technologies (such as silicon photonics and quantum circuits), many of the associated issues there are similar to those encountered in electronic IC design and can be addressed using Bayesian methods. Motivated by this observation, we present a comprehensive review of Bayesian methods in electronic design automation (EDA). By doing so, we hope to equip researchers and designers with the ability to apply Bayesian methods in solving stochastic problems in electronic circuits and beyond.Comment: 24 pages, a draft version. We welcome comments and feedback, which can be sent to [email protected]

HMC-Based Accelerator Design For Compressed Deep Neural Networks

Deep Neural Networks (DNNs) offer remarkable performance of classifications and regressions in many high dimensional problems and have been widely utilized in real-word cognitive applications. In DNN applications, high computational cost of DNNs greatly hinder their deployment in resource-constrained applications, real-time systems and edge computing platforms. Moreover, energy consumption and performance cost of moving data between memory hierarchy and computational units are higher than that of the computation itself. To overcome the memory bottleneck, data locality and temporal data reuse are improved in accelerator design. In an attempt to further improve data locality, memory manufacturers have invented 3D-stacked memory where multiple layers of memory arrays are stacked on top of each other. Inherited from the concept of Process-In-Memory (PIM), some 3D-stacked memory architectures also include a logic layer that can integrate general-purpose computational logic directly within main memory to take advantages of high internal bandwidth during computation. In this dissertation, we are going to investigate hardware/software co-design for neural network accelerator. Specifically, we introduce a two-phase filter pruning framework for model compression and an accelerator tailored for efficient DNN execution on HMC, which can dynamically offload the primitives and functions to PIM logic layer through a latency-aware scheduling controller. In our compression framework, we formulate filter pruning process as an optimization problem and propose a filter selection criterion measured by conditional entropy. The key idea of our proposed approach is to establish a quantitative connection between filters and model accuracy. We define the connection as conditional entropy over filters in a convolutional layer, i.e., distribution of entropy conditioned on network loss. Based on the definition, different pruning efficiencies of global and layer-wise pruning strategies are compared, and two-phase pruning method is proposed. The proposed pruning method can achieve a reduction of 88% filters and 46% inference time reduction on VGG16 within 2% accuracy degradation. In this dissertation, we are going to investigate hardware/software co-design for neural network accelerator. Specifically, we introduce a two-phase filter pruning framework for model compres- sion and an accelerator tailored for efficient DNN execution on HMC, which can dynamically offload the primitives and functions to PIM logic layer through a latency-aware scheduling con- troller. In our compression framework, we formulate filter pruning process as an optimization problem and propose a filter selection criterion measured by conditional entropy. The key idea of our proposed approach is to establish a quantitative connection between filters and model accuracy. We define the connection as conditional entropy over filters in a convolutional layer, i.e., distribution of entropy conditioned on network loss. Based on the definition, different pruning efficiencies of global and layer-wise pruning strategies are compared, and two-phase pruning method is proposed. The proposed pruning method can achieve a reduction of 88% filters and 46% inference time reduction on VGG16 within 2% accuracy degradation

MANAGING AND LEVERAGING VARIATIONS AND NOISE IN NANOMETER CMOS

Advanced CMOS technologies have enabled high density designs at the cost of complex fabrication process. Variation in oxide thickness and Random Dopant Fluctuation (RDF) lead to variation in transistor threshold voltage Vth. Current photo-lithography process used for printing decreasing critical dimensions result in variation in transistor channel length and width. A related challenge in nanometer CMOS is that of on-chip random noise. With decreasing threshold voltage and operating voltage; and increasing operating temperature, CMOS devices are more sensitive to random on-chip noise in advanced technologies. In this thesis, we explore novel circuit techniques to manage the impact of process variation in nanometer CMOS technologies. We also analyze the impact of on-chip noise on CMOS circuits and propose techniques to leverage or manage impact of noise based on the application. True Random Number Generator (TRNG) is an interesting cryptographic primitive that leverages on-chip noise to generate random bits; however, it is highly sensitive to process variation. We explore novel metastability circuits to alleviate the impact of variations and at the same time leverage on-chip noise sources like Random Thermal Noise and Random Telegraph Noise (RTN) to generate high quality random bits. We develop stochastic models for metastability based TRNG circuits to analyze the impact of variation and noise. The stochastic models are used to analyze and compare low power, energy efficient and lightweight post-processing techniques targeted to low power applications like System on Chip (SoC) and RFID. We also propose variation aware circuit calibration techniques to increase reliability. We extended this technique to a more generic application of designing Post-Si Tunable (PST) clock buffers to increase parametric yield in the presence of process variation. Apart from one time variation due to fabrication process, transistors undergo constant change in threshold voltage due to aging/wear-out effects and RTN. Process variation affects conventional sensors and introduces inaccuracies during measurement. We present a lightweight wear-out sensor that is tolerant to process variation and provides a fine grained wear-out sensing. A similar circuit is designed to sense fluctuation in transistor threshold voltage due to RTN. Although thermal noise and RTN are leveraged in applications like TRNG, they affect the stability of sensitive circuits like Static Random Access Memory (SRAM). We analyze the impact of on-chip noise on Bit Error Rate (BER) and post-Si test coverage of SRAM cells

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

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

Detection and Diagnosis of Out-of-Specification Failures in Mixed-Signal Circuits

Verifying whether a circuit meets its intended specifications, as well as diagnosing the circuits that do not, is indispensable at every stage of integrated circuit design. Otherwise, a significant portion of fabricated circuits could fail or behave correctly only under certain conditions. Shrinking process technologies and increased integration has further complicated this task. This is especially true of mixed-signal circuits, where a slight parametric shift in an analog component can change the output significantly. We are thus rapidly approaching a proverbial wall, where migrating existing circuits to advanced technology nodes and/or designing the next generation circuits may not be possible without suitable verification and debug strategies. Traditional approaches target accuracy and not scalability, limiting their use to high-dimensional systems. Relaxing the accuracy requirement mitigates the computational cost. Simultaneously, quantifying the level of inaccuracy retains the effectiveness of these metrics. We exercise this accuracy vs. turn-around-time trade-off to deal with multiple mixed-signal problems across both the pre- and post-silicon domains. We first obtain approximate failure probability estimates along with their confidence bands using limited simulation budgets. We then generate “failure regions” that naturally explain the parametric interactions resulting in predicted failures. These two pre-silicon contributions together enable us to estimate and reduce the failure probability, which we demonstrate on a high-dimensional phase-locked loop test-case. We leverage this pre-silicon knowledge towards test-set selection and post-silicon debug to alleviate the limited controllability and observability in the post-silicon domain. We select a set of test-points that maximizes the probability of observing failures. We then use post-silicon measurements at these test-points to identify systematic deviations from pre-silicon belief. This is demonstrated using the phase-locked loop test-case, where we boost the number of failures to observable levels and use the obtained measurements to root-cause underlying parametric shifts. The pre-silicon contributions can also be extended to perform equivalence checking and to help diagnose detected model-mismatches. The resultant calibrated model allows us to apply our work to the system level as well. The equivalence checking and model-mismatch diagnosis is successfully demonstrated using a high-level abstraction model for the phase-locked loop test-case