Florian Kriebel

Reliable software for unreliable hardware: embedded code generation aiming at reliability

A compilation technique for reliability-aware software transformations is presented. An instruction-level reliability estimation technique quantifies the effects of hardware-level faults at the instruction-level while considering spatial and temporal vulnerabilities. It bridges the gap between hardware - where faults occur according to our fault model - and software (the abstraction level where we aim to increase reliability). For a given tolerable performance overhead, an optimization algorithm compiles an application software with respect to a tradeoff between performance and reliability. Compared to performance-optimized compilation, our method incurs 60%-80% lower application failures, averaged over various fault injection scenarios and fault rates.

ASER: Adaptive Soft Error Resilience for Reliability-Heterogeneous Processors in the Dark Silicon Era

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).

Reliable code generation and execution on unreliable hardware under joint functional and timing reliability considerations

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.

Leveraging variable function resilience for selective software reliability on unreliable hardware

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.

RAISE: Reliability-Aware Instruction SchEduling for unreliable hardware

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 programs 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.

Reliability-Driven Software Transformations for Unreliable Hardware

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 applications susceptibility toward failures. Compared to performance-optimized compilation, our method incurs 60% lower application failures, averaged over various fault injection scenarios and fault rates.

Power-efficient error-resiliency for H.264/AVC context-adaptive variable length coding

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.

Cross-Layer Software Dependability on Unreliable Hardware

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.

DRVS: Power-efficient reliability management through Dynamic Redundancy and Voltage Scaling under variations

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.

Revc: Computationally Reliable Video Coding on unreliable hardware platforms: A case study on error-tolerant H.264/AVC CAVLC entropy coding

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 non-negligible 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.