Measurement and analysis of soft error vulnerability of low-voltage logic and memory circuits

Scaling the supply voltage into the sub/near-threshold domain is one of the most effective methods for improving the energy efficiency of next-generation electronic microsystems. Unfortunately, the relationship between low-voltage operation and radiation-induced soft error rate is not widely known, as little research has been previously performed and reported for soft-error susceptibility of on-chip memory and logic at very low supply voltages. This information is critical for low-voltage circuit designers, as many applications that would benefit from the energy effi­ciency of sub/near-threshold also require high reliability. This work first details the design and implementation of a portable soft error reference platform, specif­ically targeting very low-voltage operation. The circuit-level details of a TSMC 65nm test-chip design are given, along with an analysis of data from experiments performed at Los Alamos Neutron Science Center (LANSCE) and the OSU Radi­ation Center. Once this soft-error rate is known, error resiliency techniques must be utilized for increased processor reliability. The design and implementation of an error-resilient, near-threshold SIMD processor in an IBM 45nm SOI process will also be covered. This prototype demonstrates both increased reliability and improved throughput over a conventional SIMD pipeline while operating in near-threshold

Methods to improve the reliability and resiliency of near/sub-threshold digital circuits

Energy consumption is one of the primary bottlenecks to both large and small scale modern compute platforms. Reducing the operating voltage of digital circuits to voltages where the supply voltage is near or below the threshold of the transistors has recently gained attention as a method to reduce the energy required for computations by as much as 6 times. However, when operating at near/sub-threshold voltages (where the supply voltage is near or below the threshold of the transistors), imperfections in transistor manufacturing, changes in temperature, and other difficult-to-predict factors cause wide variations in the timing of Complementary Metal-Oxide Semiconductor (CMOS) circuits due to an increased sensitivity at lower voltages. These increased variations result in poor aggregate performance and cause increased rates of error occurrence in computation. This work introduces several new methods to improve the reliability of near/sub-threshold circuits. The first is a design automation technique that is used to aid in low-voltage digital standard cell synthesis. Second, two circuit-level techniques are also introduced that aim to improve the reliability and resiliency of digital circuits by means of completion/error detection. These techniques are shown to improve speed and lower energy consumption at low overheads compared to previous methods. Most importantly, these circuit-level methods are specifically designed to operate at low voltages and can themselves tolerate variations and operation in harsh environments. Finally, a test-chip prototype designed in 65nm-CMOS demonstrates the practicality and feasibility of a proposed current sensing error detector

Compressive Sensing for Low Power Sensor Design

Recent sensor System-on-Chips (SoC) have enabled significant advances in energy-efficiency by incorporating various micro-powered building blocks. Unfortunately, most of these sensor systems do not address the high power cost associated with data storage and transmission, which in some cases vastly exceeds the power consumed by the rest of the SoC. In recent years, Compressive-Sensing (CS) has been proposed as a method to accomplish significant sensor data compression, achieving compression rates up to 10x depending on the signal sparsity. This work addresses conventional CS issues including non-adaptive compression rate and offers a solution. First, a feasibility study is conducted to investigate the sparsity variance of some biomedical signals. Then an adaptive CS framework is proposed, to adjust the compression rate based upon the input signal’s sparsity on-the-fly. Thirdly, a CS framework is proposed, the reconstruction of which is aided by statistics collection. It is demonstrated how to fuse sensor data and statistics information together to improve the signal-to-error ratio (SER) of reconstruction. A test chip fabricated in TSMC 65-nm technology to implement the algorithm in a SoC incorporating statistics collection block in order to improve performance of the CS algorithm. The final portion of this research devoted to study emerging application of time-of-flight cameras for depth measurement. These devices generate a 3 Dimensional (3D) point cloud that basically includes 3D details of objects in front of them. A framework to apply CS to 3D point cloud data is presented. Finally it demonstrates how the idea of adaptive CS can be used for 3D point cloud data compression

Architecture Independent Timing Speculation Techniques in VLSI Circuits.

Conventional digital circuits must ensure correct operation throughout a wide range of operating conditions including process, voltage, and temperature variation. These conditions have an effect on circuit delays, and safety margins must be put in place which come at a power and performance cost. The Razor system proposed eliminating these timing margins by running a circuit with occasional timing errors and correcting the errors when they occur. Several existing Razor style designs have been proposed, however prior to this work, Razor could not be applied blindly or automatically to designs, as the various error correction schemes modified the architecture of the target design. Because of the architectural invasiveness and design complexities of these techniques, no published Razor style system had been applied to a complete existing commercial processor. Additionally, in all prior Razor-style systems, there is a fundamental tradeoff between speculation window and short path, or minimum delay, constraints, limiting the technique’s effectiveness. This thesis introduces the concept of Razor using two-phase latch based timing. By identifying and utilizing time borrowing as an error correction mechanism, it allows for Razor to be applied without the need to reload data or replay instructions. This allows for Razor to be blindly and automatically applied to existing designs without detailed knowledge of internal architecture. Additionally, latch based Razor allows for large speculation windows, up to 100% of nominal circuit delay, because it breaks the connection between minimum delay constraints and speculation window. By demonstrating how to transform conventional flip-flop based designs, including those which make use of clock gating, to two-phase latch based timing, Razor can be automatically added to a large set of existing digital designs. Two forms of latch based Razor are proposed. First, Bubble Razor involves rippling stall cycles throughout a circuit in response to timing errors and is applied to the ARM Cortex-M3 processor, the first ever application of a Razor technique to a complete, existing processor design. Additional work applies Bubble Razor to the ARM Cortex-R4 processor. The second latch based Razor technique, Voltage Razor, uses voltage boosting to correct for timing errors.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102461/1/mfojtik_1.pd

TIMR : Time Interleaved Multi Rail

This work presents a new energy saving technique for modern digital designs. We propose Time Interleaved Multi-Rail (TIMR) - a method for providing two dynamic supply rails to a circuit. This technique uses the first supply rail to mask the transition delay while changing the voltage of the second rail. We examine the design of TIMR as well as the implementation and considerations. We propose a number of control schemes that range from traditional DVFS to "race to sleep". This thesis also shows simulations of the technique using a existing voltage regulator in order to find the time and energy overhead of implementing the design. We find a 100μs switching time delay and 118μJ energy overhead associated with changing the voltage rail. This work concludes with comparisons to current energy saving techniques

Variation-Tolerant and Voltage-Scalable Integrated Circuits Design

Ultra-low-voltage (ULV) operation where the supply voltage of the digital computing hardware is scaled down to the level near or below transistor threshold voltage (e.g. 300-500mV) is a key technique to achieve high computing energy efficiency. It has enabled many new exciting applications in the field of Internet of Things (IoT) devices and energy-constrained applications such as medical implants, environment sensors, and micro-robots. Ultra-low-voltage (ULV) operation is also commonly used with the emerging architectures that are often non Von-Neumann style to empower energy-efficient cognitive computing. One the biggest challenge in realizing ULV design is the large circuit delay variability. To guarantee functionality in the worst-case process, voltage, and temperature (PVT) condition, the traditional safety margin approach requires operating at a slower clock frequency or higher supply voltage which significantly limits the achievable energy efficiency of the hardware. To fully claim the energy efficiency of ULV, the large circuit delay variation needs to be adaptively handled. However, the existing adaptive techniques that are optimized for nominal supply voltage operation and traditional Von-Neumann architectures become inefficient for ULV designs and emerging architectures. This thesis presents adaptive techniques based on timing error detection and correction (EDAC) that are more suitable for the energy-constrained ULV designs and the emerging architectures. The proposed techniques are demonstrated in three test chips: (1) R-Processor: A 0.4V resilient processor with a voltage-scalable and low-overhead in-situ EDAC technique. It achieves 38% energy efficiency improvement or 2.3X throughput improvement as compared to the traditional safety margin approach. (2) A 450mV timing-margin-free waveform sorter for brain computer interface (BCI) microsystem. It achieves 49.3% higher energy efficiency and 35.6% higher throughput than the traditional safety margin approach. (3) Ultra-low-power and robust power-management system which consists of a microprocessor employing ULV EDAC, 63-ratio integrated switched-capacitor DC-DC converter, and a fully-digital error based regulation controller. In this thesis, we also explore circuits for emerging techniques. The first is temperature sensors for dynamic-thermal-management (DTM). The modern high-performance microprocessors suffer from ever-increasing power densities which has led to reliability concerns and increased cooling costs from excessive heat. In order to monitor and manage the thermal behavior, DTM techniques embed multiple temperature sensors and use its information. The size, accuracy, and voltage-scalability of the sensor are critical for the performance of DTM. Therefore, we propose a temperature sensor that directly senses transistor threshold voltage and the test chip demonstrates 9X smaller area, 3X higher accuracy, and 200mV lower voltage scalability (down to 400mV) than the previous state-of-art. Another area of exploration is interconnect design for ultra-dynamic-voltage-scaling (UDVS) systems. UDVS has been proposed for applications that require both high performance and high energy efficiency. UDVS can provide peak performance with nominal supply voltage when work load is high. When work load is moderate or low, UDVS systems can switch to ULV operation for higher energy efficiency. One of the critical challenges for developing UDVS systems is the inflexibility in various circuit fabrics that are often optimized for a single supply voltage. One critical example is conventional repeater based long interconnects which suffers from non-optimal performance and energy efficiency in UDVS systems. Therefore, in this thesis, we propose a reconfigurable interconnect design based on regenerators and demonstrate near optimal performance and energy efficiency across the supply voltage of 0.3V and 1V

An Energy-Efficient System with Timing-Reliable Error-Detection Sequentials

A new type of energy-efficient digital system that integrate EDS and DVS circuits has been developed. In these systems, EDS-monitored paths convert the PVT variations into timing variations. Nevertheless, the conversion can suffer from the reliability issue (extrinsic EDS-reliability). EDS circuits detect the unfavorable timing variations (so called ``error'') and guide DVS circuits to adjust the operating voltage to a proper lower level to save the energy. However, the error detection is generally susceptible to the metastability problem (intrinsic EDS-reliability) due to the synchronizer in EDS circuits. The MTBF due to metastability is exponentially related to the synchronizer delay. This dissertation proposes a new EDS circuit deployment strategy to enhance the extrinsic EDS-reliability. This strategy requires neither buffer insertion nor an extra clock and is applicable for FPGA implementations. An FPGA-based Discrete Cosine Transform with EDS and DVS circuits deployed in this fashion demonstrates up to 16.5\% energy savings over a conventional design at equivalent frequency setting and image quality, with a 0.8\% logic element and 3.5\% maximum frequency penalties. VBSs are proposed to improve the synchronizer delay under single low-voltage supply environments. A VBS consists of a Jamb latch and a switched-capacitor-based charge pump that provides a voltage boost to the Jamb Latch to speed up the metastability resolution. The charge pump can be either CVBS or MVBS. A new methodology for extracting the metastability parameters of synchronizers under changing biasing currents is proposed. For a 1-year MTBF specification, MVBS and CVBS show 2.0 to 2.7 and 5.1 to 9.8 times the delay improvement over the basic Jamb latch, respectively, without large power consumption. Optimization techniques including transistor sizing, FBB and dynamic implementation are further applied. For a common MTBF specification at typical PVT conditions, the optimized MVBS and CVBS show 2.97 to 7.57 and 4.14 to 8.13 times the delay improvement over the basic Jamb latch, respectively. In post-Layout simulations, MVBS and CVBS are 1.84 and 2.63 times faster than the basic Jamb latch, respectively

Design and optimization of approximate multipliers and dividers for integer and floating-point arithmetic

The dawn of the twenty-first century has witnessed an explosion in the number of digital devices and data. While the emerging deep learning algorithms to extract information from this vast sea of data are becoming increasingly compute-intensive, traditional means of improving computing power are no longer yielding gains at the same rate due to the diminishing returns from traditional technology scaling. To minimize the increasing gap between computational demands and the available resources, the paradigm of approximate computing is emerging as one of the potential solutions. Specifically, the resource-efficient approximate arithmetic units promise overall system efficiency, since most of the compute-intensive applications are dominated by arithmetic operations. This thesis primarily presents design techniques for approximate hardware multipliers and dividers. The thesis presents the design of two approximate integer multipliers and an approximate integer divider. These are: an error-configurable minimally-biased approximate integer multiplier (MBM), an error-configurable reduced-error approximate log based multiplier (REALM), and error-configurable integer divider INZeD. The two multiplier designs and the divider designs are based on the coupling of novel mathematically formulated error-reduction mechanisms in the classical approximate log based multiplier and dividers, respectively. They exhibit very low error bias and offer Pareto-optimal error vs. resource-efficiency trade-offs when compared with the state-of-the-art approximate integer multipliers/dividers. Further, the thesis also presents design of approximate floating-point multipliers and dividers. These designs utilize the optimized versions of the proposed MBM and REALM multipliers for mantissa multiplications and the proposed INZeD divider for mantissa division, and offer better design trade-offs than traditional precision scaling. The existing approximate integer dividers as well as the proposed INZeD suffer from unreasonably high worst-case error. This thesis presents WEID, which is a novel light-weight method for reducing worst-case error in approximate dividers. Finally, the thesis presents a methodology for selection of approximate arithmetic units for a given application. The methodology is based on a novel selection algorithm and utilizes the subrange error characterization of approximate arithmetic units, which performs error characterization independently in different segments of the input range