A Study on Buffer Distribution for RRAM-based FPGA Routing Structures

Compared to Application-Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) provide reconfigurablity at the cost of lower performance and higher power consumption. Exploiting a large number of programmable switches, routing structures are mainly responsible for the performance limitation. Hence, employing more efficient switches can drastically improve the performance and reduce the power consumption of the FPGA. Resistive Random Access Memory (RRAM)-based switches are one of the most promising candidates to improve the FPGA routing architecture thanks to their low on-resistance and non-volatility. The lower RC delay of RRAM-based routing multiplexers, as compared to CMOS-based routing structures encourages us to reconsider the buffer distribution in FPGAs. This paper proposes an approach to reduce the number of buffers in the routing path of RRAM-based FPGAs. Our architectural simulations show that the use of RRAM switches improves the critical path delay by 56% as compared to CMOS switches in standard FPGA circuits at 45-nm technology node while, at the same time, the area and power are reduced, respectively, by 17% and 9%. By adapting the buffering scheme, an extra bonus of 9% for delay reduction, 5% for power reduction and 16% for area reduction can be obtained, as compared to the conventional buffering approach for RRAM-based FPGAs

Adaptive Intelligent Systems for Extreme Environments

As embedded processors become powerful, a growing number of embedded systems equipped with artificial intelligence (AI) algorithms have been used in radiation environments to perform routine tasks to reduce radiation risk for human workers. On the one hand, because of the low price, commercial-off-the-shelf devices and components are becoming increasingly popular to make such tasks more affordable. Meanwhile, it also presents new challenges to improve radiation tolerance, the capability to conduct multiple AI tasks and deliver the power efficiency of the embedded systems in harsh environments. There are three aspects of research work that have been completed in this thesis: 1) a fast simulation method for analysis of single event effect (SEE) in integrated circuits, 2) a self-refresh scheme to detect and correct bit-flips in random access memory (RAM), and 3) a hardware AI system with dynamic hardware accelerators and AI models for increasing flexibility and efficiency. The variances of the physical parameters in practical implementation, such as the nature of the particle, linear energy transfer and circuit characteristics, may have a large impact on the final simulation accuracy, which will significantly increase the complexity and cost in the workflow of the transistor level simulation for large-scale circuits. It makes it difficult to conduct SEE simulations for large-scale circuits. Therefore, in the first research work, a new SEE simulation scheme is proposed, to offer a fast and cost-efficient method to evaluate and compare the performance of large-scale circuits which subject to the effects of radiation particles. The advantages of transistor and hardware description language (HDL) simulations are combined here to produce accurate SEE digital error models for rapid error analysis in large-scale circuits. Under the proposed scheme, time-consuming back-end steps are skipped. The SEE analysis for large-scale circuits can be completed in just few hours. In high-radiation environments, bit-flips in RAMs can not only occur but may also be accumulated. However, the typical error mitigation methods can not handle high error rates with low hardware costs. In the second work, an adaptive scheme combined with correcting codes and refreshing techniques is proposed, to correct errors and mitigate error accumulation in extreme radiation environments. This scheme is proposed to continuously refresh the data in RAMs so that errors can not be accumulated. Furthermore, because the proposed design can share the same ports with the user module without changing the timing sequence, it thus can be easily applied to the system where the hardware modules are designed with fixed reading and writing latency. It is a challenge to implement intelligent systems with constrained hardware resources. In the third work, an adaptive hardware resource management system for multiple AI tasks in harsh environments was designed. Inspired by the “refreshing” concept in the second work, we utilise a key feature of FPGAs, partial reconfiguration, to improve the reliability and efficiency of the AI system. More importantly, this feature provides the capability to manage the hardware resources for deep learning acceleration. In the proposed design, the on-chip hardware resources are dynamically managed to improve the flexibility, performance and power efficiency of deep learning inference systems. The deep learning units provided by Xilinx are used to perform multiple AI tasks simultaneously, and the experiments show significant improvements in power efficiency for a wide range of scenarios with different workloads. To further improve the performance of the system, the concept of reconfiguration was further extended. As a result, an adaptive DL software framework was designed. This framework can provide a significant level of adaptability support for various deep learning algorithms on an FPGA-based edge computing platform. To meet the specific accuracy and latency requirements derived from the running applications and operating environments, the platform may dynamically update hardware and software (e.g., processing pipelines) to achieve better cost, power, and processing efficiency compared to the static system

Circuit Design, Architecture and CAD for RRAM-based FPGAs

Field Programmable Gate Arrays (FPGAs) have been indispensable components of embedded systems and datacenter infrastructures. However, energy efficiency of FPGAs has become a hard barrier preventing their expansion to more application contexts, due to two physical limitations: (1) The massive usage of routing multiplexers causes delay and power overheads as compared to ASICs. To reduce their power consumption, FPGAs have to operate at low supply voltage but sacrifice performance because the transistors drive degrade when working voltage decreases. (2) Using volatile memory technology forces FPGAs to lose configurations when powered off and to be reconfigured at each power on. Resistive Random Access Memories (RRAMs) have strong potentials in overcoming the physical limitations of conventional FPGAs. First of all, RRAMs grant FPGAs non-volatility, enabling FPGAs to be "Normally powered off, Instantly powered on". Second, by combining functionality of memory and pass-gate logic in one unique device, RRAMs can greatly reduce area and delay of routing elements. Third, when RRAMs are embedded into datpaths, the performance of circuits can be independent from their working voltage, beyond the limitations of CMOS circuits. However, researches and development of RRAM-based FPGAs are in their infancy. Most of area and performance predictions were achieved without solid circuit-level simulations and sophisticated Computer Aided Design (CAD) tools, causing the predicted improvements to be less convincing. In this thesis,we present high-performance and low-power RRAM-based FPGAs fromtransistorlevel circuit designs to architecture-level optimizations and CAD tools, using theoretical analysis, industrial electrical simulators and novel CAD tools. We believe that this is the first systematic study in the field, covering: From a circuit design perspective, we propose efficient RRAM-based programming circuits and routing multiplexers through both theoretical analysis and electrical simulations. The proposed 4T(ransitor)1R(RAM) programming structure demonstrates significant improvements in programming current, when compared to most popular 2T1R programming structure. 4T1R-based routingmultiplexer designs are proposed by considering various physical design parasitics, such as intrinsic capacitance of RRAMs and wells doping organization. The proposed 4T1R-based multiplexers outperformbest CMOS implementations significantly in area, delay and power at both nominal and near-Vt regime. From a CAD perspective, we develop a generic FPGA architecture exploration tool, FPGASPICE, modeling a full FPGA fabric with SPICE and Verilog netlists. FPGA-SPICE provides different levels of testbenches and techniques to split large SPICE netlists, in order to obtain better trade-off between simulation time and accuracy. FPGA-SPICE can capture area and power characteristics of SRAM-based and RRAM-based FPGAs more accurately than the currently best analyticalmodels. From an architecture perspective, we propose architecture-level optimizations for RRAMbased FPGAs and quantify their minimumrequirements for RRAM devices. Compared to the best SRAM-based FPGAs, an optimized RRAM-based FPGA architecture brings significant reduction in area, delay and power respectively. In particular, RRAM-based FPGAs operating in the near-Vt regime demonstrate a 5x power improvement without delay overhead as compared to optimized SRAM-based FPGA operating at nominal working voltage

Characterisation of Novel Resistive Switching Memory Devices

Resistive random access memory (RRAM) is widely considered as a disruptive technology that will revolutionize not only non-volatile data storage, but also potentially digital logic and neuromorphic computing. The resistive switching mechanism is generally conceived as the rupture/restoration of defect-formed conductive filament (CF) or defect profile modulation, for filamentary and non-filamentary devices respectively. However, details of the underlying microscopic behaviour of the resistive switching in RRAM are still largely missing. In this thesis, a defect probing technique based on the random telegraph noise (RTN) is developed for both filamentary and non-filamentary devices, which can reveal the resistive switching mechanism at defect level and can also be used to analyse the device performance issues. HfO2 is one of the most matured metal-oxide materials in semiconductor industry and HfO2 RRAM shows promising potential in practical application. An RTN-based defect extraction technique is developed for the HfO2 devices to detect individual defect movement and provide statistical information of CF modification during normal operations. A critical filament region (CFR) is observed and further verified by defect movement tracking. Both defect movements and CFR modification are correlated with operation conditions, endurance failure and recovery. Non-filamentary devices have areal switching characteristics, and are promising in overcoming the drawbacks of filamentary devices that mainly come from the stochastic nature of the CF. a-VMCO is an outstanding non-filamentary device with a set of unique characteristics, but its resistive switching mechanism has not been clearly understood yet. By utilizing the RTN-based defect profiling technique, defect profile modulation in the switching layer is identified and correlated with digital and analogue switching behaviours, for the first time. State instability is analysed and a stable resistance window of 10 for >106 cycles is restored through combining optimizations of device structure and operation conditions, paving the way for its practical application. TaOx-based RRAM has shown fast switching in the sub-nanosecond regime, good CMOS compatibility and record endurance of more than 1012 cycles. Several inconsistent models have been proposed for the Ta2O5/TaOx bilayered structure, and it is difficult to quantify and optimize the performance, largely due to the lack of microscopic description of resistive switching based on experimental results. An indepth analysis of the TiN/Ta2O5/TaOx/TiN structured RRAM is carried out with the RTN-based defect probing technique, for both bipolar and unipolar switching modes. Significant differences in defect profile have been observed and explanations have been provided

DESIGN AND TEST OF DIGITAL CIRCUITS AND SYSTEMS USING CMOS AND EMERGING RESISTIVE DEVICES

The memristor is an emerging nano-device. Low power operation, high density, scalability, non-volatility, and compatibility with CMOS Technology have made it a promising technology for memory, Boolean implementation, computing, and logic systems. This dissertation focuses on testing and design of such applications. In particular, we investigate on testing of memristor-based memories, design of memristive implementation of Boolean functions, and reliability and design of neuromorphic computing such as neural network. In addition, we show how to modify threshold logic gates to implement more functions. Although memristor is a promising emerging technology but is prone to defects due to uncertainties in nanoscale fabrication. Fast March tests are proposed in Chapter 2 that benefit from fast write operations. The test application time is reduced significantly while simultaneously reducing the average test energy per cell. Experimental evaluation in 45 nm technology show a speed-up of approximately 70% with a decrease in energy by approximately 40%. DfT schemes are proposed to implement the new test methods. In Chapter 3, an Integer Linear Programming based framework to identify current-mode threshold logic functions is presented. It is shown that threshold logic functions can be implemented in CMOS-based current mode logic with reduced transistor count when the input weights are not restricted to be integers. Experimental results show that many more functions can be implemented with predetermined hardware overhead, and the hardware requirement of a large percentage of existing threshold functions is reduced when comparing to the traditional CMOS-based threshold logic implementation. In Chapter 4, a new method to implement threshold logic functions using memristors is presented. This method benefits from the high range of memristor’s resistivity which is used to define different weight values, and reduces significantly the transistor count. The proposed approach implements many more functions as threshold logic gates when comparing to existing implementations. Experimental results in 45 nm technology show that the proposed memristive approach implements threshold logic gates with less area and power consumption. Finally, Chapter 5 focuses on current-based designs for neural networks. CMOS aging impacts the total synaptic current and this impacts the accuracy. Chapter 5 introduces an enhanced memristive crossbar array (MCA) based analog neural network architecture to improve reliability due to the aging effect. A built-in current-based calibration circuit is introduced to restore the total synaptic current. The calibration circuit is a current sensor that receives the ideal reference current for non-aged column and restores the reduced sensed current at each column to the ideal value. Experimental results show that the proposed approach restores the currents with less than 1% precision, and the area overhead is negligible