Ultra-Low Power Circuit Design for Cubic-Millimeter Wireless Sensor Platform.

Modern daily life is surrounded by smaller and smaller computing devices. As Bell’s Law predicts, the research community is now looking at tiny computing platforms and mm3-scale sensor systems are drawing an increasing amount of attention since they can create a whole new computing environment. Designing mm3-scale sensor nodes raises various circuit and system level challenges and we have addressed and proposed novel solutions for many of these challenges to create the first complete 1.0mm3 sensor system including a commercial microprocessor. We demonstrate a 1.0mm3 form factor sensor whose modular die-stacked structure allows maximum volume utilization. Low power I2C communication enables inter-layer serial communication without losing compatibility to standard I2C communication protocol. A dual microprocessor enables concurrent computation for the sensor node control and measurement data processing. A multi-modal power management unit allowed energy harvesting from various harvesting sources. An optical communication scheme is provided for initial programming, synchronization and re-programming after recovery from battery discharge. Standby power reduction techniques are investigated and a super cut-off power gating scheme with an ultra-low power charge pump reduces the standby power of logic circuits by 2-19× and memory by 30%. Different approaches for designing low-power memory for mm3-scale sensor nodes are also presented in this work. A dual threshold voltage gain cell eDRAM design achieves the lowest eDRAM retention power and a 7T SRAM design based on hetero-junction tunneling transistors reduces the standby power of SRAM by 9-19× with only 15% area overhead. We have paid special attention to the timer for the mm3-scale sensor systems and propose a multi-stage gate-leakage-based timer to limit the standard deviation of the error in hourly measurement to 196ms and a temperature compensation scheme reduces temperature dependency to 31ppm/°C. These techniques for designing ultra-low power circuits for a mm3-scale sensor enable implementation of a 1.0mm3 sensor node, which can be used as a skeleton for future micro-sensor systems in variety of applications. These microsystems imply the continuation of the Bell’s Law, which also predicts the massive deployment of mm3-scale computing systems and emergence of even smaller and more powerful computing systems in the near future.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91438/1/sori_1.pd

ENERGY-EFFICIENT FEATURE EXTRACTION ENGINE AND SECURE CHIP IDENTIFICATION FOR UBIQUITOUS SURVEILLANCE

Ph.DDOCTOR OF PHILOSOPH

Low-power and high-performance SRAM design in high variability advanced CMOS technology

As process technologies shrink, the size and number of memories on a chip are exponentially increasing. Embedded SRAMs are a critical component in modern digital systems, and they strongly impact the overall power, performance, and area. To promote memory-related research in academia, this dissertation introduces OpenRAM, a flexible, portable and open-source memory compiler and characterization methodology for generating and verifying memory designs across different technologies.In addition, SRAM designs, focusing on improving power consumption, access time and bitcell stability are explored in high variability advanced CMOS technologies. To have a stable read/write operation for SRAM in high variability process nodes, a differential-ended single-port 8T bitcell is proposed that improves the read noise margin, write noise margin and readout bitcell current by 45%, 48% and 21%, respectively, compared to a conventional 6T bitcell. Also, a differential-ended single-port 12T bitcell for subthreshold operation is proposed that solves the half-select disturbance and allows efficient bit-interleaving. 12T bitcell has a leakage control mechanism which helps to reduce the power consumption and provides operation down to 0.3 V. Both 8T and 12T bitcells are analyzed in a 64 kb SRAM array using 32 nm technology. Besides, to further improve the access time and power consumption, two tracking circuits (multi replica bitline delay and reconfigurable replica bitline delay techniques) are proposed to aid the generation of accurate and optimum sense amplifier set time.An error tolerant SRAM architecture suitable for low voltage video application with dynamic power-quality management is also proposed in this dissertation. This memory uses three power supplies to improve the SRAM stability in low voltages. The proposed triple-supply approach achieves 63% improvement in image quality and 69% reduction in power consumption compared to a single-supply 64 kb SRAM array at 0.70 V

Exploiting Natural On-chip Redundancy for Energy Efficient Memory and Computing

Power density is currently the primary design constraint across most computing segments and the main performance limiting factor. For years, industry has kept power density constant, while increasing frequency, lowering transistors supply (Vdd) and threshold (Vth) voltages. However, Vth scaling has stopped because leakage current is exponentially related to it. Transistor count and integration density keep doubling every process generation (Moore’s Law), but the power budget caps the amount of hardware that can be active at the same time, leading to dark silicon. With each new generation, there are more resources available, but we cannot fully exploit their performance potential. In the last years, different research trends have explored how to cope with dark silicon and unlock the energy efficiency of the chips, including Near-Threshold voltage Computing (NTC) and approximate computing. NTC aggressively lowers Vdd to values near Vth. This allows a substantial reduction in power, as dynamic power scales quadratically with supply voltage. The resultant power reduction could be used to activate more chip resources and potentially achieve performance improvements. Unfortunately, Vdd scaling is limited by the tight functionality margins of on-chip SRAM transistors. When scaling Vdd down to values near-threshold, manufacture-induced parameter variations affect the functionality of SRAM cells, which eventually become not reliable. A large amount of emerging applications, on the other hand, features an intrinsic error-resilience property, tolerating a certain amount of noise. In this context, approximate computing takes advantage of this observation and exploits the gap between the level of accuracy required by the application and the level of accuracy given by the computation, providing that reducing the accuracy translates into an energy gain. However, deciding which instructions and data and which techniques are best suited for approximation still poses a major challenge. This dissertation contributes in these two directions. First, it proposes a new approach to mitigate the impact of SRAM failures due to parameter variation for effective operation at ultra-low voltages. We identify two levels of natural on-chip redundancy: cache level and content level. The first arises because of the replication of blocks in multi-level cache hierarchies. We exploit this redundancy with a cache management policy that allocates blocks to entries taking into account the nature of the cache entry and the use pattern of the block. This policy obtains performance improvements between 2% and 34%, with respect to block disabling, a technique with similar complexity, incurring no additional storage overhead. The latter (content level redundancy) arises because of the redundancy of data in real world applications. We exploit this redundancy compressing cache blocks to fit them in partially functional cache entries. At the cost of a slight overhead increase, we can obtain performance within 2% of that obtained when the cache is built with fault-free cells, even if more than 90% of the cache entries have at least a faulty cell. Then, we analyze how the intrinsic noise tolerance of emerging applications can be exploited to design an approximate Instruction Set Architecture (ISA). Exploiting the ISA redundancy, we explore a set of techniques to approximate the execution of instructions across a set of emerging applications, pointing out the potential of reducing the complexity of the ISA, and the trade-offs of the approach. In a proof-of-concept implementation, the ISA is shrunk in two dimensions: Breadth (i.e., simplifying instructions) and Depth (i.e., dropping instructions). This proof-of-concept shows that energy can be reduced on average 20.6% at around 14.9% accuracy loss

On the Real Time Object Detection and Tracking

Object detection and tracking is widely used for detecting motions of objects present in images and video.Since last so many decades, numerous real time object detection and tracking methods have been proposed byresearchers. The proposed methods for objects to be tracked till date require some preceding informationassociated with moving objects. In real time object detection and tracking approach segmentation is the initialtask followed by background modeling for the extraction of predefined information including shape of the objects,position in the starting frame, texture, geometry and so on for further processing of the cluster pixels and videosequence of these objects. The object detection and tracking can be applied in the fields like computerized videosurveillance, traffic monitoring, robotic vision, gesture identification, human-computer interaction, militarysurveillance system, vehicle navigation, medical imaging, biomedical image analysis and many more. In thispaper we focus detailed technical review of different methods proposed for detection and tracking of objects. Thecomparison of various techniques of detection and tracking is the purpose of this work

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

Circuits and Systems Advances in Near Threshold Computing

Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing

Algorithm/Architecture Co-Design for Low-Power Neuromorphic Computing

The development of computing systems based on the conventional von Neumann architecture has slowed down in the past decade as complementary metal-oxide-semiconductor (CMOS) technology scaling becomes more and more difficult. To satisfy the ever-increasing demands in computing power, neuromorphic computing has emerged as an attractive alternative. This dissertation focuses on developing learning algorithm, hardware architecture, circuit components, and design methodologies for low-power neuromorphic computing that can be employed in various energy-constrained applications. A top-down approach is adopted in this research. Starting from the algorithm-architecture co-design, a hardware-friendly learning algorithm is developed for spiking neural networks (SNNs). The possibility of estimating gradients from spike timings is explored. The learning algorithm is developed for the ease of hardware implementation, as well as the compatibility with many well-established learning techniques developed for classic artificial neural networks (ANNs). An SNN hardware equipped with the proposed on-chip learning algorithm is implemented in CMOS technology. In this design, two unique features of SNNs, the event-driven computation and the inferring with a progressive precision, are leveraged to reduce the energy consumption. In addition to low-power SNN hardware, accelerators for ANNs are also presented to accelerate the adaptive dynamic programing algorithm. An efficient and flexible single-instruction-multiple-data architecture is proposed to exploit the inherent data-level parallelism in the inference and learning of ANNs. In addition, the accelerator is augmented with a virtual update technique, which helps improve the throughput and energy efficiency remarkably. Lastly, two techniques in the architecture-circuit level are introduced to mitigate the degraded reliability of the memory system in a neuromorphic hardware owing to the aggressively-scaled supply voltage and integration density. The first method uses on-chip feedback to compensate for the process variation and the second technique improves the throughput and energy efficiency of a conventional error-correction method.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144149/1/zhengn_1.pd

Optimization Techniques for Parallel Programming of Embedded Many-Core Computing Platforms

Nowadays many-core computing platforms are widely adopted as a viable solution to accelerate compute-intensive workloads at different scales, from low-cost devices to HPC nodes. It is well established that heterogeneous platforms including a general-purpose host processor and a parallel programmable accelerator have the potential to dramatically increase the peak performance/Watt of computing architectures. However the adoption of these platforms further complicates application development, whereas it is widely acknowledged that software development is a critical activity for the platform design. The introduction of parallel architectures raises the need for programming paradigms capable of effectively leveraging an increasing number of processors, from two to thousands. In this scenario the study of optimization techniques to program parallel accelerators is paramount for two main objectives: first, improving performance and energy efficiency of the platform, which are key metrics for both embedded and HPC systems; second, enforcing software engineering practices with the aim to guarantee code quality and reduce software costs. This thesis presents a set of techniques that have been studied and designed to achieve these objectives overcoming the current state-of-the-art. As a first contribution, we discuss the use of OpenMP tasking as a general-purpose programming model to support the execution of diverse workloads, and we introduce a set of runtime-level techniques to support fine-grain tasks on high-end many-core accelerators (devices with a power consumption greater than 10W). Then we focus our attention on embedded computer vision (CV), with the aim to show how to achieve best performance by exploiting the characteristics of a specific application domain. To further reduce the power consumption of parallel accelerators beyond the current technological limits, we describe an approach based on the principles of approximate computing, which implies modification to the program semantics and proper hardware support at the architectural level

Building Scalable Architectures Using Emerging Memory Technologies

A confluence of trends is reshaping computing today. On one end, the massive amounts of data being generated by the proliferation of sensing and internet services are creating a demand for better computer architectures and systems. The other stream of the confluence is the nanotechnology advances that are unearthing new memory device technologies with the potential to replace (or be combined with) conventional memories. Given these trends, this thesis examines emerging memory device technologies that provide a unique opportunity to build computer architectures with efficient and scalable data storage and processing capabilities. The associated memory architectures of these new systems promise to offer distinctive features such as intrinsic non-volatility, highly dense memory structures, extremely low-power consumption and even embedded processing capabilities. Among others, some examples of emerging memory technologies with such features are PCM, 3D Xpoint, STT-RAM and ReRAM. A central question with the new memory architectures built with emerging memory technologies is whether or not the resultant systems are scalable. Towards answering this question, this thesis identifies that conventional memory architecture specific scaling methods may not directly apply in case of emerging memory technologies. These methods were developed mostly for SRAM and DRAM, and today, they do not provide the desired outcomes for emerging memory technologies. As a result, there exist fundamental unsolved problems concerning scalability in building memory architectures. Unfortunately, this means that even though emerging memory technologies provide distinctive features, they may be largely left untapped. Given the scalability concerns, this thesis then advocates a scalability-first approach for building computer architectures using emerging memory technologies while being aware of the limitations and opportunities associated with them. As demonstrations of the scalability-first approach, the thesis discusses several scalability problems encountered in systems using emerging memory technologies. It also brings out potential solutions for each of these problems in the form of novel techniques and tools. For instance, the thesis discusses the problem and a solution for scaling write order enforcement mechanisms for data persistence on large non-volatile main memory systems, followed by the problem and a potential solution for scaling write bandwidth and thereby reducing memory interference on systems with dense non-volatile memory caches. Also discussed are methods for scaling system architectures with in-memory processing capability subject to its operational complexity and other limits. The proposed scalability-first approach points to prospects and ways for better adoption of emerging memory technologies within existing systems. The approach and the solutions also lead to likely transition paths to even more scalable and markedly different systems of the future