Soft error, namely silent corruption of signal or datum in a computer system, cannot be caverlierly ignored as compute and communication density grow exponentially. Soft error detection has been studied in the context of enterprise computing, high-performance computing and more recently in convolutional neural networks related to autonomous driving.Deep learning recommendation systems (DLRMs) have by now become ubiquitous and serve billions of users per day. Nevertheless, DLRM-specific soft error detection methods are hitherto missing. To fill the gap, this paper presents the first set of soft-error detection methods for low-precision quantized-arithmetic operators in DLRM including general matrix multiplication (GEMM) and EmbeddingBag. A practical method must detect error and do so with low overhead lest reduced inference speed degrades user experience. Exploiting the characteristics of both quantized arithmetic and the operators, we achieved more than 95% detection accuracy for GEMM with an overhead below 20%. For EmbeddingBag, we achieved 99% effectiveness in significant-bit-flips detection with less than 10% of false positives, while keeping overhead below 26%.

System-level validation is the most challenging phase of design verification. A common methodology in this context entails simulating the design under validation in lockstep with a high-level golden model, while comparing the architectural state of the two models at regular intervals. However, if a bug is detected, the diagnosis of the problem with this framework is extremely time and resource consuming. To address this challenge, we propose a novel bug triaging solution that collects multiple architectural-level mismatches and employs a classifier to pinpoint buggy design units. We also design and implement an automated synthetic bug injection framework that enables us to generate large datasets for training our classifier models. Experimental results show that our solution is able to correctly identify the source of a bug over 70% of the time in an out-of-order processor model. Furthermore, our solution can identify the top 3 most likely units with over 90% accuracy.

Approximate computing can be employed for an emerging class of applications from various domains such as multimedia, machine learning and computer vision. The approximated output of such applications, even though not 100% numerically correct, is often either useful or the difference is unnoticeable to the end user. This opens up a new design dimension to trade off application performance and energy consumption with output correctness. However, a largely unaddressed challenge is quality control: how to ensure the user experience meets a prescribed level of quality. Current approaches either do not monitor output quality or use sampling approaches to check a small subset of the output assuming that it is representative. While these approaches have been shown to produce average errors that are acceptable, they often miss large errors without any means to take corrective actions. To overcome this challenge, we propose Rumba for online detection and correction of large approximation errors in an approximate accelerator-based computing environment. Rumba employs continuous lightweight checks in the accelerator to detect large approximation errors and then fixes these errors by exact re-computation on the host processor. Rumba employs computationally inexpensive output error prediction models for efficient detection. Computing patterns amenable for approximation (e.g., map and stencil) are usually data parallel in nature and Rumba exploits this property for selective correction. Overall, Rumba is able to achieve 2.1x reduction in output error for an unchecked approximation accelerator while maintaining the accelerator performance gains at the cost of reducing the energy savings from 3.2x to 2.2x for a set of applications from different approximate computing domains.

A growing number of applications from various domains such as multimedia, machine learning and computer vision are inherently fault tolerant. However, for these soft workloads, not all computations are fault tolerant (e.g., A loop trip count). In this paper, we propose a compiler-based approach that takes advantage of soft computations inherent in the aforementioned class of workloads to bring down the cost of software-only transient fault detection. The technique works by identifying a small subset of critical variables that are necessary for correct macro-operation of the program. Traditional duplication and comparison are used to protect these variables. For the remaining variables and temporaries that only affect the micro-operation of the program, strategic expected value checks are inserted into the code. Intuitively, a computation-chain result near the expected value is either correct or close enough to the correct result so that it does not matter for non-critical variables. Overall, the proposed solution has, on average, only 19.5% performance overhead and reduces the number of silent data corruptions from 15% down to 7.3% and user-visible silent data corruptions from 3.4% down to 1.2% in comparison to an unmodified application. This unacceptable silent data corruption rate is even lower than a traditional full duplication scheme that has, on average, 57% overhead.

The continual trend of shrinking feature sizes and reducing voltage levels makes transistors faster and more efficient. However, it also makes them more susceptible to transient hardware faults. Transient faults due to high energy particle strikes or circuit crosstalk can corrupt the output of a program or cause it to crash. Previous studies have reported that as much as 70% of the transient faults disturb program control flow, making it critical to protect control flow. Traditional approaches employ signatures to check that every control flow transfer in a program is valid. While having high fault coverage, large performance overheads are introduced by such detailed checking. We propose a coarse-grain control flow checking method to detect transient faults in a cost effective way. Our software-only approach is centered on the principle of abstraction: control flow that exhibits simple run-time properties (e.g., proper path length) is almost always completely correct. Our solution targets off-the-shelf commodity embedded systems to provide a low cost protection against transient faults. The proposed technique achieves its efficiency by simplifying signature calculations in each basic block and by performing checking at a coarse-grain level. The coarse-grain signature comparison points are obtained by the use of a region based analysis. In addition, we propose a technique to protect control flow transfers via call and return instructions to ensure all control flow is covered by our technique. Overall, our proposed technique has an average of 11% performance overhead in comparison to 75% performance overhead of previously proposed signature based techniques while maintaining approximately the same degree of fault coverage.

As cache hierarchies become deeper and the number of cores on a chip increases, managing caches becomes more important for performance and energy. However, current hardware cache management policies do not always adapt optimally to the applications behavior: e.g., caches may be polluted by data structures whose locality cannot be captured by the caches, and producer-consumer communication incurs multiple round trips of coherence messages per cache line transferred. We propose load and store instructions that carry hints regarding into which cache(s) the accessed data should be placed. Our instructions allow software to convey locality information to the hardware, while incurring minimal hardware cost and not affecting correctness. Our instructions provide a 1.07x speedup and a 1.24x energy efficiency boost, on average, according to simulations on a 64-core system with private L1 and L2 caches. With a large shared L3 cache added, the benefits increase, providing 1.33x energy reduction on average.

Successive generations of processors use smaller transistors in the quest to make more powerful computing systems. It has been previously studied that smaller transistors make processors more susceptible to soft errors (transient faults caused by high energy particle strikes). Such errors can result in unexpected behavior and incorrect results. With smaller and cheaper transistors becoming pervasive in mainstream computing, it is necessary to protect these devices against soft errors; an increasing rate of faults necessitates the protection of applications running on commodity processors against soft errors. The existing methods of protecting against such faults generally have high area or performance overheads and thus are not directly applicable in the embedded design space. In order to protect against soft errors, the detection of these errors is a necessary first step so that a recovery can be triggered.

The complexity of modern chips intensifies verification challenges, and an increasing share of this verification effort is shouldered by post-silicon validation. Focusing on the first silicon prototypes, post-silicon validation poses critical new challenges such as intermittent failures, where multiple executions of a same test do not yield a consistent outcome. These are often due to on-chip asynchronous events and electrical effects, leading to extremely time-consuming, if not unachievable, bug diagnosis and debugging processes. In this work, we propose a methodology called BPS (Bug Positioning System) to support the automatic diagnosis of these difficult bugs. During post-silicon validation, lightweight BPS hardware logs a compact encoding of observed signal activity over multiple executions of the same test: some passing, some failing. Leveraging a novel post-analysis algorithm, BPS uses the logged activity to diagnose the bug, identifying the approximate manifestation time and critical design signals. We found experimentally that BPS can localize most bugs down to the exact root signal and within about 1,000 clock cycles of their occurrence.