10,192 research outputs found
Soft Error Analysis and Mitigation at High Abstraction Levels
Radiation-induced soft errors, as one of the major reliability challenges in future technology nodes, have to be carefully taken into consideration in the design space exploration. This thesis presents several novel and efficient techniques for soft error evaluation and mitigation at high abstract levels, i.e. from register transfer level up to behavioral algorithmic level. The effectiveness of proposed techniques is demonstrated with extensive synthesis experiments
On the Resilience of RTL NN Accelerators: Fault Characterization and Mitigation
Machine Learning (ML) is making a strong resurgence in tune with the massive
generation of unstructured data which in turn requires massive computational
resources. Due to the inherently compute- and power-intensive structure of
Neural Networks (NNs), hardware accelerators emerge as a promising solution.
However, with technology node scaling below 10nm, hardware accelerators become
more susceptible to faults, which in turn can impact the NN accuracy. In this
paper, we study the resilience aspects of Register-Transfer Level (RTL) model
of NN accelerators, in particular, fault characterization and mitigation. By
following a High-Level Synthesis (HLS) approach, first, we characterize the
vulnerability of various components of RTL NN. We observed that the severity of
faults depends on both i) application-level specifications, i.e., NN data
(inputs, weights, or intermediate), NN layers, and NN activation functions, and
ii) architectural-level specifications, i.e., data representation model and the
parallelism degree of the underlying accelerator. Second, motivated by
characterization results, we present a low-overhead fault mitigation technique
that can efficiently correct bit flips, by 47.3% better than state-of-the-art
methods.Comment: 8 pages, 6 figure
Evaluating application vulnerability to soft errors in multi-level cache hierarchy
As the capacity of cache increases dramatically with new processors, soft errors originating in cache has become a major reliability concern for high performance processors. This paper presents application specific soft error vulnerability analysis in order to understand an application's responses to soft errors from different levels of caches. Based on a high-performance processor simulator called Graphite, we have implemented a fault injection framework that can selectively inject bit flips to different levels of caches. We simulated a wide range of relevant bit error patterns and measured the applications' vulnerabilities to bit errors. Our experimental results have shown the various vulnerabilities of applications to bit errors from different levels of caches; the results have also indicated the probabilities of different behaviors from the applications
High-Level Analysis of the Impact of Soft-Faults in Cyberphysical Systems
As digital systems grow in complexity and are used in a broader variety of safety-critical applications, there is an ever-increasing demand for assessing the dependability and safety of such systems, especially when subjected to hazardous environments. As a result, it is important to identify and correct any functional abnormalities and component faults as early as possible in order to minimize performance degradation and to avoid potential perilous situations. Existing techniques often lack the capacity to perform a comprehensive
and exhaustive analysis on complex redundant architectures, leading to less than optimal risk evaluation. Hence, an early analysis of dependability of such safety-critical applications enables designers to develop systems that meets high dependability requirements. Existing techniques in the field often lack the capacity to perform full system analyses due to state-explosion limitations (such as transistor and gate-level analyses), or due to the time and monetary costs attached to them (such as simulation, emulation, and physical testing).
In this work we develop a system-level methodology to model and analyze the effects of Single Event Upsets (SEUs) in cyberphysical system designs. The proposed methodology investigates the impacts of SEUs in the entire system model (fault tree level), including SEU propagation paths, logical masking of errors, vulnerability to specific events, and critical nodes. The methodology also provides insights on a system's weaknesses, such as the impact of each component to the system's vulnerability, as well as hidden sources of failure, such as latent faults. Moreover, the proposed methodology is able to identify and categorize the system's components in order of criticality, and to evaluate different approaches to the mitigation of such criticality (in the form of different configurations of TMR) in order to obtain the most efficient mitigation solution available.
The proposed methodology is also able to model and analyze system components individually (system component level), in order to more accurately estimate the component's vulnerability to SEUs. In this case, a more refined analysis of the component is conducted, which enables us to identify the source of the component's criticality. Thereafter, a second mitigation mechanic (internal to the component) takes place, in order to evaluate the gains and costs of applying different configurations of TMR to the component internally. Finally, our approach will draw a comparison between the results obtained at both levels of analysis in order to evaluate the most efficient way of improving the targeted system design
Multi-core devices for safety-critical systems: a survey
Multi-core devices are envisioned to support the development of next-generation safety-critical systems, enabling the on-chip integration of functions of different criticality. This integration provides multiple system-level potential benefits such as cost, size, power, and weight reduction. However, safety certification becomes a challenge and several fundamental safety technical requirements must be addressed, such as temporal and spatial independence, reliability, and diagnostic coverage. This survey provides a categorization and overview at different device abstraction levels (nanoscale, component, and device) of selected key research contributions that support the compliance with these fundamental safety requirements.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-65316-P, Basque Government under grant KK-2019-00035 and the HiPEAC Network of Excellence. The Spanish Ministry of Economy and Competitiveness has also partially supported Jaume Abella under Ramon y Cajal postdoctoral fellowship (RYC-2013-14717).Peer ReviewedPostprint (author's final draft
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
We empirically evaluate an undervolting technique, i.e., underscaling the
circuit supply voltage below the nominal level, to improve the power-efficiency
of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable
Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing
faults due to excessive circuit latency increase. We evaluate the
reliability-power trade-off for such accelerators. Specifically, we
experimentally study the reduced-voltage operation of multiple components of
real FPGAs, characterize the corresponding reliability behavior of CNN
accelerators, propose techniques to minimize the drawbacks of reduced-voltage
operation, and combine undervolting with architectural CNN optimization
techniques, i.e., quantization and pruning. We investigate the effect of
environmental temperature on the reliability-power trade-off of such
accelerators. We perform experiments on three identical samples of modern
Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification
CNN benchmarks. This approach allows us to study the effects of our
undervolting technique for both software and hardware variability. We achieve
more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain
is the result of eliminating the voltage guardband region, i.e., the safe
voltage region below the nominal level that is set by FPGA vendor to ensure
correct functionality in worst-case environmental and circuit conditions. 43%
of the power-efficiency gain is due to further undervolting below the
guardband, which comes at the cost of accuracy loss in the CNN accelerator. We
evaluate an effective frequency underscaling technique that prevents this
accuracy loss, and find that it reduces the power-efficiency gain from 43% to
25%.Comment: To appear at the DSN 2020 conferenc
Cross-Layer Approaches for an Aging-Aware Design of Nanoscale Microprocessors
Thanks to aggressive scaling of transistor dimensions, computers have revolutionized our life. However, the increasing unreliability of devices fabricated in nanoscale technologies emerged as a major threat for the future success of computers. In particular, accelerated transistor aging is of great importance, as it reduces the lifetime of digital systems. This thesis addresses this challenge by proposing new methods to model, analyze and mitigate aging at microarchitecture-level and above
A systems engineering approach to servitisation system modelling and reliability assessment
Companies are changing their business model in order to improve their long term
competitiveness. Once where they provided only products, they are now
providing a service with that product resulting in a reduced cost of ownership.
Such a business case benefits both customer and service supplier only if the
availability of the product, and hence the service, is optimised. For highly
integrated product and service offerings this means it is necessary to assess the
reliability monitoring service which underpins service availability.
Reliability monitoring service assessment requires examination of not only
product monitoring capability but also the effectiveness of the maintenance
response prompted by the detection of fault conditions. In order to address these
seemingly dissimilar aspects of the reliability monitoring service, a methodology
is proposed which defines core aspects of both the product and service
organisation. These core aspects provide a basis from which models of both the
product and service organisation can be produced.
The models themselves though not functionally representative, portray the
primary components of each type of system, the ownership of these system
components and how they are interfaced. These system attributes are then
examined to establish system risk to reliability by inspection, evaluation of the
model or by reference to model source documentation.
The result is a methodology that can be applied to such large scale, highly
integrated systems at either an early stage of development or in latter
development stages. The methodology will identify weaknesses in each system
type, indicating areas which should be considered for system redesign and will
also help inform the analyst of whether or not the reliability monitoring service as
a whole meets the requirements of the proposed business case
- …