Interconnect yield analysis and fault tolerance for field programmable gate arrays

Imperial Users onl

Reconfiguration and Fine-Grained Redundancy for Fault Tolerance in FPGAs

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Asynchronous techniques for new generation variation-tolerant FPGA

PhD ThesisThis thesis presents a practical scenario for asynchronous logic implementation that would benefit the modern Field-Programmable Gate Arrays (FPGAs) technology in improving reliability. A method based on Asynchronously-Assisted Logic (AAL) blocks is proposed here in order to provide the right degree of variation tolerance, preserve as much of the traditional FPGAs structure as possible, and make use of asynchrony only when necessary or beneficial for functionality. The newly proposed AAL introduces extra underlying hard-blocks that support asynchronous interaction only when needed and at minimum overhead. This has the potential to avoid the obstacles to the progress of asynchronous designs, particularly in terms of area and power overheads. The proposed approach provides a solution that is complementary to existing variation tolerance techniques such as the late-binding technique, but improves the reliability of the system as well as reducing the design’s margin headroom when implemented on programmable logic devices (PLDs) or FPGAs. The proposed method suggests the deployment of configurable AAL blocks to reinforce only the variation-critical paths (VCPs) with the help of variation maps, rather than re-mapping and re-routing. The layout level results for this method's worst case increase in the CLB’s overall size only of 6.3%. The proposed strategy retains the structure of the global interconnect resources that occupy the lion’s share of the modern FPGA’s soft fabric, and yet permits the dual-rail iv completion-detection (DR-CD) protocol without the need to globally double the interconnect resources. Simulation results of global and interconnect voltage variations demonstrate the robustness of the method

Delay Measurements and Self Characterisation on FPGAs

This thesis examines new timing measurement methods for self delay characterisation of Field-Programmable Gate Arrays (FPGAs) components and delay measurement of complex circuits on FPGAs. Two novel measurement techniques based on analysis of a circuit's output failure rate and transition probability is proposed for accurate, precise and efficient measurement of propagation delays. The transition probability based method is especially attractive, since it requires no modifications in the circuit-under-test and requires little hardware resources, making it an ideal method for physical delay analysis of FPGA circuits. The relentless advancements in process technology has led to smaller and denser transistors in integrated circuits. While FPGA users benefit from this in terms of increased hardware resources for more complex designs, the actual productivity with FPGA in terms of timing performance (operating frequency, latency and throughput) has lagged behind the potential improvements from the improved technology due to delay variability in FPGA components and the inaccuracy of timing models used in FPGA timing analysis. The ability to measure delay of any arbitrary circuit on FPGA offers many opportunities for on-chip characterisation and physical timing analysis, allowing delay variability to be accurately tracked and variation-aware optimisations to be developed, reducing the productivity gap observed in today's FPGA designs. The measurement techniques are developed into complete self measurement and characterisation platforms in this thesis, demonstrating their practical uses in actual FPGA hardware for cross-chip delay characterisation and accurate delay measurement of both complex combinatorial and sequential circuits, further reinforcing their positions in solving the delay variability problem in FPGAs

Within-Die Delay Variation Measurement And Analysis For Emerging Technologies Using An Embedded Test Structure

Both random and systematic within-die process variations (PV) are growing more severe with shrinking geometries and increasing die size. Escalation in the variations in delay and power with reductions in feature size places higher demands on the accuracy of variation models. Their availability can be used to improve yield, and the corresponding profitability and product quality of the fabricated integrated circuits (ICs). Sources of within-die variations include optical source limitations, and layout-based systematic effects (pitch, line-width variability, and microscopic etch loading). Unfortunately, accurate models of within-die PVs are becoming more difficult to derive because of their increasingly sensitivity to design-context. Embedded test structures (ETS) continue to play an important role in the development of models of PVs and as a mechanism to improve correlations between hardware and models. Variations in path delays are increasing with scaling, and are increasingly affected by neighborhood\u27 interactions. In order to fully characterize within-die variations, delays must be measured in the context of actual core-logic macros. Doing so requires the use of an embedded test structure, as opposed to traditional scribe line test structures such as ring oscillators (RO). Accurate measurements of within-die variations can be used, e.g., to better tune models to actual hardware (model-to-hardware correlations). In this research project, I propose an embedded test structure called REBEL (Regional dELay BEhavior) that is designed to measure path delays in a minimally invasive fashion; and its architecture measures the path delays more accurately. Design for manufacture-ability (DFM) analysis is done on the on 90 nm ASIC chips and 28nm Zynq 7000 series FPGA boards. I present ASIC results on within-die path delay variations in a floating-point unit (FPU) fabricated in IBM\u27s 90 nm technology, with 5 pipeline stages, used as a test vehicle in chip experiments carried out at nine different temperature/voltage (TV) corners. Also experimental data has been analyzed for path delay variations in short vs long paths. FPGA results on within-die variation and die-to-die variations on Advanced Encryption System (AES) using single pipelined stage are also presented. Other analysis that have been performed on the calibrated path delays are Flip Flop propagation delays for both rising and falling edge (tpHL and tpLH), uncertainty analysis, path distribution analysis, short versus long path variations and mid-length path within-die variation. I also analyze the impact on delay when the chips are subjected to industrial-level temperature and voltage variations. From the experimental results, it has been established that the proposed REBEL provides capabilities similar to an off-chip logic analyzer, i.e., it is able to capture the temporal behavior of the signal over time, including any static and dynamic hazards that may occur on the tested path. The ASIC results further show that path delays are correlated to the launch-capture (LC) interval used to time them. Therefore, calibration as proposed in this work must be carried out in order to obtain an accurate analysis of within-die variations. Results on ASIC chips show that short paths can vary up to 35% on average, while long paths vary up to 20% at nominal temperature and voltage. A similar trend occurs for within-die variations of mid-length paths where magnitudes reduced to 20% and 5%, respectively. The magnitude of delay variations in both these analyses increase as temperature and voltage are changed to increase performance. The high level of within-die delay variations are undesirable from a design perspective, but they represent a rich source of entropy for applications that make use of \u27secrets\u27 such as authentication, hardware metering and encryption. Physical unclonable functions (PUFs) are a class of primitives that leverage within-die-variations as a means of generating random bit strings for these types of applications, including hardware security and trust. Zynq FPGAs Die-to-Die and within-die variation study shows that on average there is 5% of within-Die variation and the range of die-to-Die variation can go upto 3ns. The die-to-Die variations can be explored in much further detail to study the variations spatial dependance. Additionally, I also carried out research in the area data mining to cater for big data by focusing the work on decision tree classification (DTC) to speed-up the classification step in hardware implementation. For this purpose, I devised a pipelined architecture for the implementation of axis parallel binary decision tree classification for meeting up with the requirements of execution time and minimal resource usage in terms of area. The motivation for this work is that analyzing larger data-sets have created abundant opportunities for algorithmic and architectural developments, and data-mining innovations, thus creating a great demand for faster execution of these algorithms, leading towards improving execution time and resource utilization. Decision trees (DT) have since been implemented in software programs. Though, the software implementation of DTC is highly accurate, the execution times and the resource utilization still require improvement to meet the computational demands in the ever growing industry. On the other hand, hardware implementation of DT has not been thoroughly investigated or reported in detail. Therefore, I propose a hardware acceleration of pipelined architecture that incorporates the parallel approach in acquiring the data by having parallel engines working on different partitions of data independently. Also, each engine is processing the data in a pipelined fashion to utilize the resources more efficiently and reduce the time for processing all the data records/tuples. Experimental results show that our proposed hardware acceleration of classification algorithms has increased throughput, by reducing the number of clock cycles required to process the data and generate the results, and it requires minimal resources hence it is area efficient. This architecture also enables algorithms to scale with increasingly large and complex data sets. We developed the DTC algorithm in detail and explored techniques for adapting it to a hardware implementation successfully. This system is 3.5 times faster than the existing hardware implementation of classification.\u2

DIGITALLY ASSISTED TECHNIQUES FOR NYQUIST RATE ANALOG-to-DIGITAL CONVERTERS

With the advance of technology and rapid growth of digital systems, low power high speed analog-to-digital converters with great accuracy are in demand. To achieve high effective number of bits Analog-to-Digital Converter(ADC) calibration as a time consuming process is a potential bottleneck for designs. This dissertation presentsa fully digital background calibration algorithm for a 7-bit redundant flash ADC using split structure and look-up table based correction. Redundant comparators are used in the flash ADC design of this work in order to tolerate large offset voltages while minimizing signal input capacitance. The split ADC structure helps by eliminating the unknown input signal from the calibration path. The flash ADC has been designed in 180nm IBM CMOS technology and fabricated through MOSIS. This work was supported by Analog Devices, Wilmington,MA. While much research on ADC design has concentrated on increasing resolution and sample rate, there are many applications (e.g. biomedical devices and sensor networks) that do not require high performance but do require low power energy efficient ADCs. This dissertation also explores on design of a low quiescent current 100kSps Successive Approximation (SAR) ADC that has been used as an error detection ADC for an automotive application in 350nm CD (CMOS-DMOS) technology. This work was supported by ON Semiconductor Corp, East Greenwich,RI

A design methodology for robust, energy-efficient, application-aware memory systems

Memory design is a crucial component of VLSI system design from area, power and performance perspectives. To meet the increasingly challenging system specifications, architecture, circuit and device level innovations are required for existing memory technologies. Emerging memory solutions are widely explored to cater to strict budgets. This thesis presents design methodologies for custom memory design with the objective of power-performance benefits across specific applications. Taking example of STTRAM (spin transfer torque random access memory) as an emerging memory candidate, the design space is explored to find optimal energy design solution. A thorough thermal reliability study is performed to estimate detection reliability challenges and circuit solutions are proposed to ensure reliable operation. Adoption of the application-specific optimal energy solution is shown to yield considerable energy benefits in a read-heavy application called MBC (memory based computing). Circuit level customizations are studied for the volatile SRAM (static random access memory) memory, which will provide improved energy-delay product (EDP) for the same MBC application. Memory design has to be aware of upcoming challenges from not only the application nature but also from the packaging front. Taking 3D die-folding as an example, SRAM performance shift under die-folding is illustrated. Overall the thesis demonstrates how knowledge of the system and packaging can help in achieving power efficient and high performance memory design.Ph.D

Predicting power scalability in a reconfigurable platform

This thesis focuses on the evolution of digital hardware systems. A reconfigurable platform is proposed and analysed based on thin-body, fully-depleted silicon-on-insulator Schottky-barrier transistors with metal gates and silicide source/drain (TBFDSBSOI). These offer the potential for simplified processing that will allow them to reach ultimate nanoscale gate dimensions. Technology CAD was used to show that the threshold voltage in TBFDSBSOI devices will be controllable by gate potentials that scale down with the channel dimensions while remaining within appropriate gate reliability limits. SPICE simulations determined that the magnitude of the threshold shift predicted by TCAD software would be sufficient to control the logic configuration of a simple, regular array of these TBFDSBSOI transistors as well as to constrain its overall subthreshold power growth. Using these devices, a reconfigurable platform is proposed based on a regular 6-input, 6-output NOR LUT block in which the logic and configuration functions of the array are mapped onto separate gates of the double-gate device. A new analytic model of the relationship between power (P), area (A) and performance (T) has been developed based on a simple VLSI complexity metric of the form ATσ = constant. As σ defines the performance “return” gained as a result of an increase in area, it also represents a bound on the architectural options available in power-scalable digital systems. This analytic model was used to determine that simple computing functions mapped to the reconfigurable platform will exhibit continuous power-area-performance scaling behavior. A number of simple arithmetic circuits were mapped to the array and their delay and subthreshold leakage analysed over a representative range of supply and threshold voltages, thus determining a worse-case range for the device/circuit-level parameters of the model. Finally, an architectural simulation was built in VHDL-AMS. The frequency scaling described by σ, combined with the device/circuit-level parameters predicts the overall power and performance scaling of parallel architectures mapped to the array

An Ultra-Low-Energy, Variation-Tolerant FPGA Architecture Using Component-Specific Mapping

As feature sizes scale toward atomic limits, parameter variation continues to increase, leading to increased margins in both delay and energy. Parameter variation both slows down devices and causes devices to fail. For applications that require high performance, the possibility of very slow devices on critical paths forces designers to reduce clock speed in order to meet timing. For an important and emerging class of applications that target energy-minimal operation at the cost of delay, the impact of variation-induced defects at very low voltages mandates the sizing up of transistors and operation at higher voltages to maintain functionality. With post-fabrication configurability, FPGAs have the opportunity to self-measure the impact of variation, determining the speed and functionality of each individual resource. Given that information, a delay-aware router can use slow devices on non-critical paths, fast devices on critical paths, and avoid known defects. By mapping each component individually and customizing designs to a component's unique physical characteristics, we demonstrate that we can eliminate delay margins and reduce energy margins caused by variation. To quantify the potential benefit we might gain from component-specific mapping, we first measure the margins associated with parameter variation, and then focus primarily on the energy benefits of FPGA delay-aware routing over a wide range of predictive technologies (45 nm--12 nm) for the Toronto20 benchmark set. We show that relative to delay-oblivious routing, delay-aware routing without any significant optimizations can reduce minimum energy/operation by 1.72x at 22 nm. We demonstrate how to construct an FPGA architecture specifically tailored to further increase the minimum energy savings of component-specific mapping by using the following techniques: power gating, gate sizing, interconnect sparing, and LUT remapping. With all optimizations considered we show a minimum energy/operation savings of 2.66x at 22 nm, or 1.68--2.95x when considered across 45--12 nm. As there are many challenges to measuring resource delays and mapping per chip, we discuss methods that may make component-specific mapping more practical. We demonstrate that a simpler, defect-aware routing achieves 70% of the energy savings of delay-aware routing. Finally, we show that without variation tolerance, scaling from 16 nm to 12 nm results in a net increase in minimum energy/operation; component-specific mapping, however, can extend minimum energy/operation scaling to 12 nm and possibly beyond.</p

Reliability in the face of variability in nanometer embedded memories

In this thesis, we have investigated the impact of parametric variations on the behaviour of one performance-critical processor structure - embedded memories. As variations manifest as a spread in power and performance, as a first step, we propose a novel modeling methodology that helps evaluate the impact of circuit-level optimizations on architecture-level design choices. Choices made at the design-stage ensure conflicting requirements from higher-levels are decoupled. We then complement such design-time optimizations with a runtime mechanism that takes advantage of adaptive body-biasing to lower power whilst improving performance in the presence of variability. Our proposal uses a novel fully-digital variation tracking hardware using embedded DRAM (eDRAM) cells to monitor run-time changes in cache latency and leakage. A special fine-grain body-bias generator uses the measurements to generate an optimal body-bias that is needed to meet the required yield targets. A novel variation-tolerant and soft-error hardened eDRAM cell is also proposed as an alternate candidate for replacing existing SRAM-based designs in latency critical memory structures. In the ultra low-power domain where reliable operation is limited by the minimum voltage of operation (Vddmin), we analyse the impact of failures on cache functional margin and functional yield. Towards this end, we have developed a fully automated tool (INFORMER) capable of estimating memory-wide metrics such as power, performance and yield accurately and rapidly. Using the developed tool, we then evaluate the #effectiveness of a new class of hybrid techniques in improving cache yield through failure prevention and correction. Having a holistic perspective of memory-wide metrics helps us arrive at design-choices optimized simultaneously for multiple metrics needed for maintaining lifetime requirements