Soft errors and aging are two substantial reliability-related problems in today's computing systems. While soft errors are transient in nature, they can lead to wrong application outputs and even application failures. However, aging has permanent effects and results in slower performance and timing errors in processors. In this paper, we will discuss different approaches how these reliability threats can be mitigated in multi-core systems for performance/power-constrained scenarios. Different research challenges and corresponding solutions will be described which jointly target the above-mentioned reliability issues while also considering additional problems like process variation and dark silicon. These solutions are implemented in different system layers and make use of an analysis of and adaptation to the application properties. Moreover, they leverage the opportunities offered by multi-core systems, for instance, to efficiently enable redundant multithreading.

The architectural differences between ASICs and FPGAs limit the effective performance gains achievable by the application of ASIC-based approximation principles for FPGA-based reconfigurable computing systems. This paper presents a novel approximate multiplier architecture customized towards the FPGA-based fabrics, an efficient design methodology, and an open-source library. Our designs provide higher area, latency and energy gains along with better output accuracy than those offered by the state-of-the-art ASIC-based approximate multipliers. Moreover, compared to the multiplier IP offered by the Xilinx Vivado, our proposed design achieves up to 30%, 53%, and 67% gains in terms of area, latency, and energy, respectively, while incurring an insignificant accuracy loss (on average, below 1% average relative error). Our library of approximate multipliers is open-source and available online at https://cfaed.tudresden.de/pd-downloads to fuel further research and development in this area, and thereby enabling a new research direction for the FPGA community.

With the advancements in the process technology, fault-tolerance against transient errors has emerged as an important design requirement for computing systems fabricated using nano-scale devices. Traditionally, redundancy-based techniques have been employed to detect and correct errors, and to achieve full system protection. However, as fault masking properties on different system levels have been observed and applications with lower accuracy demands or error-tolerant properties exist, reliability-heterogeneous architectures have recently paved the way for power-efficient dependable systems. In this paper, we will discuss the building blocks of such processors (both embedded and superscalar) with different fault-tolerant modes on the architecture level covering memory components like caches as well as in-order and out-of-order processor designs. We analyze the soft error vulnerability of different components and show how the variations in vulnerabilities can be exploited to improve the performance and power efficiency of such processors. We additionally show that a reliability-driven compiler can be leveraged to realize software-level heterogeneous fault tolerance by generating different reliable application versions with diverse reliability and performance properties.

This paper presents a fault-tolerance technique for H.264's Context-Adaptive Variable Length Coding (CAVLC) on unreliable computing hardware. The application-specific knowledge is leveraged at both algorithm and architecture levels to protect the CAVLC process (especially context adaptation and coding tables) in a reliable yet power-efficient manner. Specifically, the statistical analysis of coding syntax and video content properties are exploited for: (1) selective redundancy of coefficient/header data of video bitstreams; (2) partitioning the coding tables into various sub-tables to reduce the power overhead of fault tolerance; and (3) run-time power management of memory parts storing the sub-tables and their parity computations. Experimental results demonstrate that leveraging application-specific knowledge reduces area and performance overhead by 2x compared to a double-parity table protection technique. For functional verification and area comparison, the complete H.264 CAVLC architecture is prototyped on a Xilinx Virtex-5 FPGA (though not limited to it).

Reliability optimization in the nano-era needs to account for multiple reliability concerns. Redundant Multithreading (RMT) has emerged as a promising technique to mitigate soft-errors in multi-cores. Since variation- and aging-unawareness during RMT may increase aging of slow cores due to high utilization or lead to unbalanced aging under varying workload scenarios, we propose to leverage variations in vulnerability and duty cycle by means of multiple compiled versions. We perform variation-aware task mapping to proactively reduce the aging of slower cores and thereby maintaining the minimum processing capabilities. Afterwards, we perform an aging-aware activation/deactivation of RMT considering tasks' variable resilience properties and select appropriate reliable versions for the mapped tasks. Experimental results demonstrate that compared to state-of-the-art aging-unaware RMT techniques, our ageOpt-RMT provides improved and balanced aging profiles by 2x on average.

This paper presents a cross-layer reliability modeling and optimization approach that leverages multiple software layers like compiler and run-time system to improve the overall reliability considering unreliable or partially-reliable hardware. In order to bridge the gap between hardware and software to achieve high efficiency, our technique incorporates the knowledge from hardware layers during reliability modeling and design of optimization techniques. We demonstrate how different software layers operate synergistically to achieve a high degree of reliability.

Due to the architectural design, process variations and aging, individual cores in many-core systems exhibit heterogeneous performance. In many-core systems, a commonly adopted soft error mitigation technique is Redundant Multithreading (RMT) that achieves error detection and recovery through redundant thread execution on different cores for an application. However,task mappingandthe task execution mode(i.e., whether a task executes in a reliable mode with RMT or unreliable mode without RMT) need to be considered for achieving resource-efficient reliability. This paper explores how to efficiently assign the tasks onto different cores with heterogeneous performance properties and determine the execution modes of tasks in order to achieve high reliability and satisfy the tolerance of timeliness. We demonstrate that the task mapping problem under heterogeneous performance can be solved by employing Hungarian Algorithm as subroutine to efficiently assign the tasks onto the cores to optimize the system reliability with polynomial time complexity. To obtain the efficient task execution modes, we also propose an iterative mode adaptation technique and guarantee the tolerable timing constraint. Our results illustrate that compared to state-of-the-art, the proposed approaches achieve up to$80$percent reliability improvement (on average$20$percent) under different scenarios of chip frequency variation maps.

To enable reliable embedded systems, it is imperative to leverage the compiler and system software for joint optimization of functional correctness (i.e., vulnerability indexes) and timing correctness (i.e., deadline misses). This paper considers the optimization of the reliability-timing (RT) penalty, defined as a linear combination of the vulnerability and deadline misses. We propose a cross-layer approach to achieve reliable code generation and execution at compilation and system software layers for embedded systems. This is enabled by the concept of generating multiple versions for given application functions, with diverse performance and reliability tradeoffs, by exploiting different reliability-guided compilation options. As the execution time of a function is not fixed, the selection of the versions depends upon the execution behavior of the previous functions. Based on the reliability and execution time profiling of these versions, our reliability-driven system software decides the prioritization of the functions for determining their execution order and employs dynamic version selection to dynamically select a suitable version of a function. Specifically, our scheme builds a schedule table offline to optimize the RT penalty, and uses this table at run time to select suitable versions for the subsequent functions. A complex real-world application of “secure video and audio processing” composed of various functions is evaluated for reliable code generation and execution.

On account of their large footprint, on-chip last-level caches in multi-core systems are one of the most vulnerable components to soft errors. However, vulnerability to soft errors highly depends on the configuration and parameters of the last-level cache, especially when executing different applications concurrently. In this article we propose a novel reliability-aware reconfigurable last-level cache architecture (R 2 Cache) and cache vulnerability model for multi-cores. R 2 Cache supports various reliability-wise efficient cache configurations (i.e., cache parameter selection and cache partitioning) for different concurrently executing applications. The proposed vulnerability model takes into account the vulnerability of both the data and tag arrays as well as the active cache area for applications in different execution phases. To enable runtime adaptations, we introduce a lightweight online vulnerability predictor that exploits the knowledge of performance metrics like number of L2 misses to accurately estimate the cache vulnerability to soft errors. Based on the predicted vulnerabilities of different concurrently executing applications in the current execution epoch, our runtime reliability manager reconfigures the cache such that, for the next execution epoch, the total vulnerability for all concurrently executing applications is minimized under user-provided tolerable performance/energy overheads. In scenarios where single-bit error correction for cache lines may be afforded, vulnerability-aware reconfigurations can be leveraged to increase the reliability of the last-level cache against multi-bit errors. Compared to state-of-the-art vulnerability-minimizing and reconfigurable caches, the proposed architecture provides 35.27% and 23.42% vulnerability savings, respectively, when averaged across numerous experiments, while reducing the vulnerability by more than 65% and 60%, respectively, for selected applications and application phases.

Elevated power densities result in the so-called Dark Silicon constraint that prohibits simultaneous activation of all the cores in an on-chip system (in the full performance mode) to respect the safe thermal limits, thus enforcing a significant amount of on-chip resources to stay 'dark' (i.e., power-gated). In this paper, we show that how Dark Silicon together with the manufacturing process induced variability can be harnessed to mitigate reliability threats in the nano-era. In particular, we propose a run-time system Hayat* that harnesses Dark Silicon to decelerate and/or balance temperature-dependent aging, while also considering variability in order to improve the overall system performance for a given lifetime. Experimental evaluation across a range of chips to account for process variations illustrates that our Hayat system can provide a significant aging/performance improvement and decelerates the chip aging by 6 months -- 5 years (depending upon the required lifetime constraint) compared to state-of-the-art techniques.

Many-core processors facilitate coarse-grained reliability by exploiting available cores for redundant multithreading. However, ensuring high reliability with reduced power consumption necessitates joint considerations of variations in vulnerability, performance and power properties of software as well as the underlying hardware. In this paper, we propose a power-efficient reliability management system for many-core processors. It exploits various basic redundancy techniques (like, dual and triple modular redundancy) operating in different voltage-frequency levels, each offering distinct reliability, performance and power properties. Our system performs Dynamic Redundancy and Voltage Scaling (DRVS) considering process variations in hardware, and diversities in software vulnerability and execution time properties. Experiments show that DRVS system provides significant reliability improvements while providing up to 60% reduced power consumption compared to state-of-the-art techniques.

In the emerging Dark Silicon era, not all parts of an on-chip system (i.e., cores, Network-on-Chip, and memory resources) can be simultaneously powered-on at the full speed. This paper aims at exposing dark silicon challenges to the NOCS community with an overview of some of the early research efforts that are attempting to shape the design and run-time management of future generation heterogeneous dark silicon processors. The goal is to cover both the computation and communication perspectives. In particular, we exploit computation and communication heterogeneity at multiple levels of system abstractions to design and manage dark silicon processors. The available dark silicon is leveraged to improve power/energy, performance, and reliability efficiency.

The Dark Silicon provides opportunities to realize Reliability-Heterogeneous Processors with ISA compatible cores having different levels of protection against reliability threats (like soft errors). This paper presents design-time customization of Reliability-Heterogeneous Processors given a set of applications and area constraints. A run-time system adaptively manages the soft error resilience under a given thermal design power (TDP) budget. We synthesize an embedded processor with different levels of protection and present area and power results for a 45nm technology. We illustrate the benefits of adaptive soft error resilience by comparing it with four different state-of-the-art approaches where we achieve 58%-96% overall system reliability improvements under a tight TDP constraint (corresponding to a 65% dark area).

Designing dependable on-chip manycore systems is subjected to consideration of multiple reliability threats, i.e. soft errors, aging, process variation, etc. In this paper, we introduce a novel adaptive Dependability Tuning (dTune) scheme for many-core processors. It leverages the knowledge of varying vulnerability and error masking properties of different applications along with multiple compiled versions (each offering distinct reliability and performance properties). Our dTune system dynamically tunes the dependability mode at the hardware level through hybrid Redundant Multithreading tuning and at the software level through selection of reliable code version under given performance constraints. It jointly accounts for soft errors and cores' performance variations due to design-time process variation and/or run-time aging-induced performance degradation. We compare our dTune system with four different state-of-the-art techniques and achieve on average 44% and up to 63% improved task reliability for different chip configurations, different variability maps, and different aging years.

This paper presents a novel Dynamic Reliability Management System (DyReMS) for on-chip systems that performs resilience-driven resource allocation and mapping. It accounts for both the tasks' resilience properties and heterogeneous error recovery features of different cores. DyReMS also chooses a reliable task version (out of multiple reliability-aware transformed options) depending upon the reliability level of the allocated core. In case of error detection, rollbacks are performed. Our system provides 70%-87% improved task reliability compared to a timing reliability-optimizing core assignment, i.e. minimizing the probability of deadline misses (with EDF scheduling).

We propose multiple reliability-driven software transformations targeting unreliable hardware. These transformations reduce the executions of critical instructions and spatial/temporal vulnerabilities of different instructions with respect to different processor components. The goal is to lower the application's susceptibility toward failures. Compared to performance-optimized compilation, our method incurs 60% lower application failures, averaged over various fault injection scenarios and fault rates.

State-of-the-art reliability optimizing schemes deploy spatial or temporal redundancy for the complete functionality. This introduces significant performance/area overhead which is often prohibitive within the stringent design constraints of embedded systems. This paper presents a novel scheme for selective software reliability optimization constraint under user-provided tolerable performance overhead constraint. To enable this scheme, statistical models for quantifying software resilience and error masking properties at function and instruction level are proposed. These models leverage a whole new range of reliability optimization. Given a tolerable performance overhead, our scheme selectively protects the reliability-wise most important instructions based on their masking probability, vulnerability, and redundancy overhead. Compared to state-of-the-art [7], our scheme provides a 4.84X improved reliability at 50% tolerable performance overhead constraint.

To enable reliable embedded systems, it is imperative to leverage the compiler and system software for joint optimization of functional correctness, i.e., vulnerability indexes, and timing correctness, i.e., the deadline misses. This paper considers the optimization of the Reliability-Timing (RT) penalty, defined as a linear combination of the vulnerability indexes (reliability penalties) and the deadline misses. We propose a multi-layer approach to achieve reliable code generation and execution at compilation and system software layers for embedded systems. This is enabled by the concept of generating multiple versions, for given application functions, with diverse performance and reliability tradeoffs, by exploiting different reliability-guided compilation options. Based on the reliability and execution time profiling of these versions, our reliability-driven system software employs dynamic version selections to dynamically select a suitable version of a function according to the execution behavior of the previous functions. Specifically, our scheme builds a schedule table offline to optimize the RT penalty, and uses this table at run time to select suitable versions for the subsequent functions properly. A complex real-world application of “secure video and audio processing” composed of various functions is evaluated for reliable code generation and execution. The reliability analysis and evaluation is performed on a reliability-aware processor simulator.

A compile-time Reliability-Aware Instruction SchEduling (RAISE) scheme is presented, which takes into account the spatial and temporal vulnerabilities of different processor resources (pipeline, register file, etc.) used during the execution of different instructions. It reduces the software program's susceptibility towards failures by minimizing the occupancy cycles of critical instructions inside the pipeline stages in addition to reducing the vulnerable periods of their operands. To facilitate RAISE, a novel technique for static reliability estimation during compilation is presented (i.e. before instructions scheduling). Compared to state-of-the-art reliability-aware instruction schedulers, our scheme provides up to 32.7% reduced software program failures over three different fault rates.

Technology scaling has led to unreliable computing hardware due to high susceptibility against soft errors. In this paper, we propose an error-resilient architecture for Context-Adaptive Variable Length Coding (CAVLC) in H.264/AVC. Due to its context-adaptive nature and intricate control flow CAVLC is very sensitive to soft errors. An error during the CAVLC process (especially during the context adaptation or in VLC tables) may result in severe mismatch between encoder and decoder. The primary goal in our error-resilient CAVLC architecture is to protect codeword/codelength tables and context adaptation in a reliable yet power efficient manner. For reducing the power over-head, the tables are partitioned in various sub-tables each protected with variable-sized parity. Moreover, for further power reduction, our approach incorporates state-retentive power-gating of different sub-tables at run time depending upon the statistical distribution of syntax elements. Compared to the unprotected case, our scheme provides a video quality improvement of 18dB (averaged over various fault injection cases and video sequences) at the cost of a 35% area overhead and 45% performance overhead due to the error-detection logic. However, partitioned sub-tables increase the potential for power-gating, thus bring a leakage energy saving of 58%. Compared to state-of-the-art table protection, our scheme provides 2x reduced area and performance overhead. For function-al verification and area comparison, the architecture is prototyped on a Xilinx Virtex-5 FPGA, though not limited to it. For the soft errors experiments, evaluation of error-resiliency and power efficiency, we have developed a fault injection and simulation setup.

Advancement in technology has complemented the increased complexity of state-of-the-art video coding standards by providing high-performance multimedia platforms. This has resulted in nonnegligible computational reliability issues, especially with respect to soft errors. This paper motivates the dire need towards computationally Reliable Video Coding (ReVC) on modern hardware platforms. We highlight the issues with the help of a fault injection analysis during the CAVLC processing. We present a case study on error-tolerant CAVLC, where error tolerance is achieved at various levels of abstraction, ranging from algorithm to hardware. Application-specific knowledge of CAVLC is used to detect potential errors at the algorithm level, while hardware means are provided to protect the tables for CAVLC. Experiments demonstrate that our approach significantly improves the video quality under high fault rates. Furthermore, we demonstrate that when considering the application-specific knowledge, our proposed solution incurs minimal area and performance overhead compared to redundancy-based reliability methods.