Design Techniques for Energy-Quality Scalable Digital Systems

Energy efficiency is one of the key design goals in modern computing. Increasingly complex tasks are being executed in mobile devices and Internet of Things end-nodes, which are expected to operate for long time intervals, in the orders of months or years, with the limited energy budgets provided by small form-factor batteries. Fortunately, many of such tasks are error resilient, meaning that they can toler- ate some relaxation in the accuracy, precision or reliability of internal operations, without a significant impact on the overall output quality. The error resilience of an application may derive from a number of factors. The processing of analog sensor inputs measuring quantities from the physical world may not always require maximum precision, as the amount of information that can be extracted is limited by the presence of external noise. Outputs destined for human consumption may also contain small or occasional errors, thanks to the limited capabilities of our vision and hearing systems. Finally, some computational patterns commonly found in domains such as statistics, machine learning and operational research, naturally tend to reduce or eliminate errors. Energy-Quality (EQ) scalable digital systems systematically trade off the quality of computations with energy efficiency, by relaxing the precision, the accuracy, or the reliability of internal software and hardware components in exchange for energy reductions. This design paradigm is believed to offer one of the most promising solutions to the impelling need for low-energy computing. Despite these high expectations, the current state-of-the-art in EQ scalable design suffers from important shortcomings. First, the great majority of techniques proposed in literature focus only on processing hardware and software components. Nonetheless, for many real devices, processing contributes only to a small portion of the total energy consumption, which is dominated by other components (e.g. I/O, memory or data transfers). Second, in order to fulfill its promises and become diffused in commercial devices, EQ scalable design needs to achieve industrial level maturity. This involves moving from purely academic research based on high-level models and theoretical assumptions to engineered flows compatible with existing industry standards. Third, the time-varying nature of error tolerance, both among different applications and within a single task, should become more central in the proposed design methods. This involves designing “dynamic” systems in which the precision or reliability of operations (and consequently their energy consumption) can be dynamically tuned at runtime, rather than “static” solutions, in which the output quality is fixed at design-time. This thesis introduces several new EQ scalable design techniques for digital systems that take the previous observations into account. Besides processing, the proposed methods apply the principles of EQ scalable design also to interconnects and peripherals, which are often relevant contributors to the total energy in sensor nodes and mobile systems respectively. Regardless of the target component, the presented techniques pay special attention to the accurate evaluation of benefits and overheads deriving from EQ scalability, using industrial-level models, and on the integration with existing standard tools and protocols. Moreover, all the works presented in this thesis allow the dynamic reconfiguration of output quality and energy consumption. More specifically, the contribution of this thesis is divided in three parts. In a first body of work, the design of EQ scalable modules for processing hardware data paths is considered. Three design flows are presented, targeting different technologies and exploiting different ways to achieve EQ scalability, i.e. timing-induced errors and precision reduction. These works are inspired by previous approaches from the literature, namely Reduced-Precision Redundancy and Dynamic Accuracy Scaling, which are re-thought to make them compatible with standard Electronic Design Automation (EDA) tools and flows, providing solutions to overcome their main limitations. The second part of the thesis investigates the application of EQ scalable design to serial interconnects, which are the de facto standard for data exchanges between processing hardware and sensors. In this context, two novel bus encodings are proposed, called Approximate Differential Encoding and Serial-T0, that exploit the statistical characteristics of data produced by sensors to reduce the energy consumption on the bus at the cost of controlled data approximations. The two techniques achieve different results for data of different origins, but share the common features of allowing runtime reconfiguration of the allowed error and being compatible with standard serial bus protocols. Finally, the last part of the manuscript is devoted to the application of EQ scalable design principles to displays, which are often among the most energy- hungry components in mobile systems. The two proposals in this context leverage the emissive nature of Organic Light-Emitting Diode (OLED) displays to save energy by altering the displayed image, thus inducing an output quality reduction that depends on the amount of such alteration. The first technique implements an image-adaptive form of brightness scaling, whose outputs are optimized in terms of balance between power consumption and similarity with the input. The second approach achieves concurrent power reduction and image enhancement, by means of an adaptive polynomial transformation. Both solutions focus on minimizing the overheads associated with a real-time implementation of the transformations in software or hardware, so that these do not offset the savings in the display. For each of these three topics, results show that the aforementioned goal of building EQ scalable systems compatible with existing best practices and mature for being integrated in commercial devices can be effectively achieved. Moreover, they also show that very simple and similar principles can be applied to design EQ scalable versions of different system components (processing, peripherals and I/O), and to equip these components with knobs for the runtime reconfiguration of the energy versus quality tradeoff

Autonomous Recovery Of Reconfigurable Logic Devices Using Priority Escalation Of Slack

Field Programmable Gate Array (FPGA) devices offer a suitable platform for survivable hardware architectures in mission-critical systems. In this dissertation, active dynamic redundancy-based fault-handling techniques are proposed which exploit the dynamic partial reconfiguration capability of SRAM-based FPGAs. Self-adaptation is realized by employing reconfiguration in detection, diagnosis, and recovery phases. To extend these concepts to semiconductor aging and process variation in the deep submicron era, resilient adaptable processing systems are sought to maintain quality and throughput requirements despite the vulnerabilities of the underlying computational devices. A new approach to autonomous fault-handling which addresses these goals is developed using only a uniplex hardware arrangement. It operates by observing a health metric to achieve Fault Demotion using Recon- figurable Slack (FaDReS). Here an autonomous fault isolation scheme is employed which neither requires test vectors nor suspends the computational throughput, but instead observes the value of a health metric based on runtime input. The deterministic flow of the fault isolation scheme guarantees success in a bounded number of reconfigurations of the FPGA fabric. FaDReS is then extended to the Priority Using Resource Escalation (PURE) online redundancy scheme which considers fault-isolation latency and throughput trade-offs under a dynamic spare arrangement. While deep-submicron designs introduce new challenges, use of adaptive techniques are seen to provide several promising avenues for improving resilience. The scheme developed is demonstrated by hardware design of various signal processing circuits and their implementation on a Xilinx Virtex-4 FPGA device. These include a Discrete Cosine Transform (DCT) core, Motion Estimation (ME) engine, Finite Impulse Response (FIR) Filter, Support Vector Machine (SVM), and Advanced Encryption Standard (AES) blocks in addition to MCNC benchmark circuits. A iii significant reduction in power consumption is achieved ranging from 83% for low motion-activity scenes to 12.5% for high motion activity video scenes in a novel ME engine configuration. For a typical benchmark video sequence, PURE is shown to maintain a PSNR baseline near 32dB. The diagnosability, reconfiguration latency, and resource overhead of each approach is analyzed. Compared to previous alternatives, PURE maintains a PSNR within a difference of 4.02dB to 6.67dB from the fault-free baseline by escalating healthy resources to higher-priority signal processing functions. The results indicate the benefits of priority-aware resiliency over conventional redundancy approaches in terms of fault-recovery, power consumption, and resource-area requirements. Together, these provide a broad range of strategies to achieve autonomous recovery of reconfigurable logic devices under a variety of constraints, operating conditions, and optimization criteria

Designing Flexible, Energy Efficient and Secure Wireless Solutions for the Internet of Things

The Internet of Things (IoT) is an emerging concept where ubiquitous physical objects (things) consisting of sensor, transceiver, processing hardware and software are interconnected via the Internet. The information collected by individual IoT nodes is shared among other often heterogeneous devices and over the Internet. This dissertation presents flexible, energy efficient and secure wireless solutions in the IoT application domain. System design and architecture designs are discussed envisioning a near-future world where wireless communication among heterogeneous IoT devices are seamlessly enabled. Firstly, an energy-autonomous wireless communication system for ultra-small, ultra-low power IoT platforms is presented. To achieve orders of magnitude energy efficiency improvement, a comprehensive system-level framework that jointly optimizes various system parameters is developed. A new synchronization protocol and modulation schemes are specified for energy-scarce ultra-small IoT nodes. The dynamic link adaptation is proposed to guarantee the ultra-small node to always operate in the most energy efficiency mode, given an operating scenario. The outcome is a truly energy-optimized wireless communication system to enable various new applications such as implanted smart-dust devices. Secondly, a configurable Software Defined Radio (SDR) baseband processor is designed and shown to be an efficient platform on which to execute several IoT wireless standards. It is a custom SIMD execution model coupled with a scalar unit and several architectural optimizations: streaming registers, variable bitwidth, dedicated ALUs, and an optimized reduction network. Voltage scaling and clock gating are employed to further reduce the power, with a more than a 100% time margin reserved for reliable operation in the near-threshold region. Two upper bound systems are evaluated. A comprehensive power/area estimation indicates that the overhead of realizing SDR flexibility is insignificant. The benefit of baseband SDR is quantified and evaluated. To further augment the benefits of a flexible baseband solution and to address the security issue of IoT connectivity, a light-weight Galois Field (GF) processor is proposed. This processor enables both energy-efficient block coding and symmetric/asymmetric cryptography kernel processing for a wide range of GF sizes (2^m, m = 2, 3, ..., 233) and arbitrary irreducible polynomials. Program directed connections among primitive GF arithmetic units enable dynamically configured parallelism to efficiently perform either four-way SIMD GF operations, including multiplicative inverse, or a long bit-width GF product in a single cycle. This demonstrates the feasibility of a unified architecture to enable error correction coding flexibility and secure wireless communication in the low power IoT domain.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137164/1/yajchen_1.pd

Design And Synthesis Of Clockless Pipelines Based On Self-resetting Stage Logic

For decades, digital design has been primarily dominated by clocked circuits. With larger scales of integration made possible by improved semiconductor manufacturing techniques, relying on a clock signal to orchestrate logic operations across an entire chip became increasingly difficult. Motivated by this problem, designers are currently considering circuits which can operate without a clock. However, the wide acceptance of these circuits by the digital design community requires two ingredients: (i) a unified design methodology supported by widely available CAD tools, and (ii) a granularity of design techniques suitable for synthesizing large designs. Currently, there is no unified established design methodology to support the design and verification of these circuits. Moreover, the majority of clockless design techniques is conceived at circuit level, and is subsequently so fine-grain, that their application to large designs can have unacceptable area costs. Given these considerations, this dissertation presents a new clockless technique, called self-resetting stage logic (SRSL), in which the computation of a block is reset periodically from within the block itself. SRSL is used as a building block for three coarse-grain pipelining techniques: (i) Stage-controlled self-resetting stage logic (S-SRSL) Pipelines: In these pipelines, the control of the communication between stages is performed locally between each pair of stages. This communication is performed in a uni-directional manner in order to simplify its implementation. (ii) Pipeline-controlled self-resetting stage logic (P-SRSL) Pipelines: In these pipelines, the communication between each pair of stages in the pipeline is driven by the oscillation of the last pipeline stage. Their communication scheme is identical to the one used in S-SRSL pipelines. (iii) Delay-tolerant self-resetting stage logic (D-SRSL) Pipelines: While communication in these pipelines is local in nature in a manner similar to the one used in S-SRL pipelines, this communication is nevertheless extended in both directions. The result of this bi-directional approach is an increase in the capability of the pipeline to handle stages with random delay. Based on these pipelining techniques, a new design methodology is proposed to synthesize clockless designs. The synthesis problem consists of synthesizing an SRSL pipeline from a gate netlist with a minimum area overhead given a specified data rate. A two-phase heuristic algorithm is proposed to solve this problem. The goal of the algorithm is to pipeline a given datapath by minimizing the area occupied by inter-stage latches without violating any timing constraints. Experiments with this synthesis algorithm show that while P-SRSL pipelines can reach high throughputs in shallow pipelines, D-SRSL pipelines can achieve comparable throughputs in deeper pipelines

Hardware / Software Architectural and Technological Exploration for Energy-Efficient and Reliable Biomedical Devices

Nowadays, the ubiquity of smart appliances in our everyday lives is increasingly strengthening the links between humans and machines. Beyond making our lives easier and more convenient, smart devices are now playing an important role in personalized healthcare delivery. This technological breakthrough is particularly relevant in a world where population aging and unhealthy habits have made non-communicable diseases the first leading cause of death worldwide according to international public health organizations. In this context, smart health monitoring systems termed Wireless Body Sensor Nodes (WBSNs), represent a paradigm shift in the healthcare landscape by greatly lowering the cost of long-term monitoring of chronic diseases, as well as improving patients' lifestyles. WBSNs are able to autonomously acquire biological signals and embed on-node Digital Signal Processing (DSP) capabilities to deliver clinically-accurate health diagnoses in real-time, even outside of a hospital environment. Energy efficiency and reliability are fundamental requirements for WBSNs, since they must operate for extended periods of time, while relying on compact batteries. These constraints, in turn, impose carefully designed hardware and software architectures for hosting the execution of complex biomedical applications. In this thesis, I develop and explore novel solutions at the architectural and technological level of the integrated circuit design domain, to enhance the energy efficiency and reliability of current WBSNs. Firstly, following a top-down approach driven by the characteristics of biomedical algorithms, I perform an architectural exploration of a heterogeneous and reconfigurable computing platform devoted to bio-signal analysis. By interfacing a shared Coarse-Grained Reconfigurable Array (CGRA) accelerator, this domain-specific platform can achieve higher performance and energy savings, beyond the capabilities offered by a baseline multi-processor system. More precisely, I propose three CGRA architectures, each contributing differently to the maximization of the application parallelization. The proposed Single, Multi and Interleaved-Datapath CGRA designs allow the developed platform to achieve substantial energy savings of up to 37%, when executing complex biomedical applications, with respect to a multi-core-only platform. Secondly, I investigate how the modeling of technology reliability issues in logic and memory components can be exploited to adequately adjust the frequency and supply voltage of a circuit, with the aim of optimizing its computing performance and energy efficiency. To this end, I propose a novel framework for workload-dependent Bias Temperature Instability (BTI) impact analysis on biomedical application results quality. Remarkably, the framework is able to determine the range of safe circuit operating frequencies without introducing worst-case guard bands. Experiments highlight the possibility to safely raise the frequency up to 101% above the maximum obtained with the classical static timing analysis. Finally, through the study of several well-known biomedical algorithms, I propose an approach allowing energy savings by dynamically and unequally protecting an under-powered data memory in a new way compared to regular error protection schemes. This solution relies on the Dynamic eRror compEnsation And Masking (DREAM) technique that reduces by approximately 21% the energy consumed by traditional error correction codes

Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

Stability Analysis of Jump-Linear Systems Driven by Finite-State Machines with Markovian Inputs

A control system with a fault recovery mechanism in the feedback loop and with faults occurring in a non-deterministic manner can be modeled as a class of hybrid systems, i.e., a dynamical system switched by a finite-state machine or an automaton. When the plant and controller are linear, such a system can be modeled as a jump-linear system driven by a finite-state machine with a random input process. Such fault recovery mechanisms are found in flight control systems and distributed control systems with communication networks. In these critical applications, closed-loop stability of the system in the presence of fault recoveries becomes an important issue. Finite-state machines as mathematical constructs are widely used by computer scientists to model and analyze algorithms. In particular, fault recovery mechanisms that are implemented in hardware with logic based circuits and finite memory can be modeled appropriately with finite-state machines. In this thesis, mathematical tools are developed to determine the mean-square stability of a closed-loop system, modeled as a jump-linear system in series with a finite-state machine driven by a random process. The random input process is in general assumed to be any r-th order Markov process, where r ≥ 0. While stability tests for a jump-linear system with a Markovian switching rule are well known, the main contribution of the present work arises from the fact that output of a finite-state machine driven by a Markov process is in general not Markovian. Therefore, new stability analysis tools are provided for this class of systems and demonstrated through Monte Carlo simulations

Exploring resource/performance trade-offs for streaming applications on embedded multiprocessors

Embedded system design is challenged by the gap between the ever-increasing customer demands and the limited resource budgets. The tough competition demands ever-shortening time-to-market and product lifecycles. To solve or, at least to alleviate, the aforementioned issues, designers and manufacturers need model-based quantitative analysis techniques for early design-space exploration to study trade-offs of different implementation candidates. Moreover, modern embedded applications, especially the streaming applications addressed in this thesis, face more and more dynamic input contents, and the platforms that they are running on are more flexible and allow runtime configuration. Quantitative analysis techniques for embedded system design have to be able to handle such dynamic adaptable systems. This thesis has the following contributions: - A resource-aware extension to the Synchronous Dataflow (SDF) model of computation. - Trade-off analysis techniques, both in the time-domain and in the iterationdomain (i.e., on an SDF iteration basis), with support for resource sharing. - Bottleneck-driven design-space exploration techniques for resource-aware SDF. - A game-theoretic approach to controller synthesis, guaranteeing performance under dynamic input. As a first contribution, we propose a new model, as an extension of static synchronous dataflow graphs (SDF) that allows the explicit modeling of resources with consistency checking. The model is called resource-aware SDF (RASDF). The extension enables us to investigate resource sharing and to explore different scheduling options (ways to allocate the resources to the different tasks) using state-space exploration techniques. Consistent SDF and RASDF graphs have the property that an execution occurs in so-called iterations. An iteration typically corresponds to the processing of a meaningful piece of data, and it returns the graph to its initial state. On multiprocessor platforms, iterations may be executed in a pipelined fashion, which makes performance analysis challenging. As the second contribution, this thesis develops trade-off analysis techniques for RASDF, both in the time-domain and in the iteration-domain (i.e., on an SDF iteration basis), to dimension resources on platforms. The time-domain analysis allows interleaving of different iterations, but the size of the explored state space grows quickly. The iteration-based technique trades the potential of interleaving of iterations for a compact size of the iteration state space. An efficient bottleneck-driven designspace exploration technique for streaming applications, the third main contribution in this thesis, is derived from analysis of the critical cycle of the state space, to reveal bottleneck resources that are limiting the throughput. All techniques are based on state-based exploration. They enable system designers to tailor their platform to the required applications, based on their own specific performance requirements. Pruning techniques for efficient exploration of the state space have been developed. Pareto dominance in terms of performance and resource usage is used for exact pruning, and approximation techniques are used for heuristic pruning. Finally, the thesis investigates dynamic scheduling techniques to respond to dynamic changes in input streams. The fourth contribution in this thesis is a game-theoretic approach to tackle controller synthesis to select the appropriate schedules in response to dynamic inputs from the environment. The approach transforms the explored iteration state space of a scenario- and resource-aware SDF (SARA SDF) graph to a bipartite game graph, and maps the controller synthesis problem to the problem of finding a winning positional strategy in a classical mean payoff game. A winning strategy of the game can be used to synthesize the controller of schedules for the system that is guaranteed to satisfy the throughput requirement given by the designer

Contributions to Securing Software Updates in IoT

The Internet of Things (IoT) is a large network of connected devices. In IoT, devices can communicate with each other or back-end systems to transfer data or perform assigned tasks. Communication protocols used in IoT depend on target applications but usually require low bandwidth. On the other hand, IoT devices are constrained, having limited resources, including memory, power, and computational resources. Considering these limitations in IoT environments, it is difficult to implement best security practices. Consequently, network attacks can threaten devices or the data they transfer. Thus it is crucial to react quickly to emerging vulnerabilities. These vulnerabilities should be mitigated by firmware updates or other necessary updates securely. Since IoT devices usually connect to the network wirelessly, such updates can be performed Over-The-Air (OTA). This dissertation presents contributions to enable secure OTA software updates in IoT. In order to perform secure updates, vulnerabilities must first be identified and assessed. In this dissertation, first, we present our contribution to designing a maturity model for vulnerability handling. Next, we analyze and compare common communication protocols and security practices regarding energy consumption. Finally, we describe our designed lightweight protocol for OTA updates targeting constrained IoT devices. IoT devices and back-end systems often use incompatible protocols that are unable to interoperate securely. This dissertation also includes our contribution to designing a secure protocol translator for IoT. This translation is performed inside a Trusted Execution Environment (TEE) with TLS interception. This dissertation also contains our contribution to key management and key distribution in IoT networks. In performing secure software updates, the IoT devices can be grouped since the updates target a large number of devices. Thus, prior to deploying updates, a group key needs to be established among group members. In this dissertation, we present our designed secure group key establishment scheme. Symmetric key cryptography can help to save IoT device resources at the cost of increased key management complexity. This trade-off can be improved by integrating IoT networks with cloud computing and Software Defined Networking (SDN).In this dissertation, we use SDN in cloud networks to provision symmetric keys efficiently and securely. These pieces together help software developers and maintainers identify vulnerabilities, provision secret keys, and perform lightweight secure OTA updates. Furthermore, they help devices and systems with incompatible protocols to be able to interoperate