Applying lightweight soft error mitigation techniques to embedded mixed precision deep neural networks

Deep neural networks (DNNs) are being incorporated in resource-constrained IoT devices, which typically rely on reduced memory footprint and low-performance processors. While DNNs' precision and performance can vary and are essential, it is also vital to deploy trained models that provide high reliability at low cost. To achieve an unyielding reliability and safety level, it is imperative to provide electronic computing systems with appropriate mechanisms to tackle soft errors. This paper, therefore, investigates the relationship between soft errors and model accuracy. In this regard, an extensive soft error assessment of the MobileNet model is conducted considering precision bitwidth variations (2, 4, and 8 bits) running on an Arm Cortex-M processor. In addition, this work promotes the use of a register allocation technique (RAT) that allocates the critical DNN function/layer to a pool of specific general-purpose processor registers. Results obtained from more than 4.5 million fault injections show that RAT gives the best relative performance, memory utilization, and soft error reliability trade-offs w.r.t. a more traditional replication-based approach. Results also show that the MobileNet soft error reliability varies depending on the precision bitwidth of its convolutional layers

SOFIA: An automated framework for early soft error assessment, identification, and mitigation

The occurrence of radiation-induced soft errors in electronic computing systems can either affect non-essential system functionalities or violate safety-critical conditions, which might incur life-threatening situations. To reach high safety standard levels, reliability engineers must be able to explore and identify efficient mitigation solutions to reduce the occurrence of soft errors at the initial design cycle. This paper presents SOFIA, a framework that integrates: (i) a set of fault injection techniques that enable bespoke inspections, (ii) machine learning methods to correlate soft error results and system architecture parameters, and (iii) mitigation techniques, including: full and partial triple modular redundancy (TMR) as well as a register allocation technique (RAT), which allocates the critical code (e.g., application’s function, machine learning layer) to a pool of specific processor registers. The proposed framework and novel variations of the RAT are validated through more than 1739k fault injections considering a real Linux kernel, benchmarks from different domains and a multi-core Arm processor.</p

Soft error assessment of attitude estimation algorithms running in a resource-constrained device under neutron radiation

This paper assesses the soft error reliability of attitude estimation algorithms running on a resource-constrained microprocessor under neutron radiation. Results suggest that the EKF algorithm has the best trade-off between reliability and runtime overhead.</p

A lightweight mitigation technique for resource-constrained devices executing DNN inference models under neutron radiation

Deep neural network (DNN) models are being deployed in safety-critical embedded devices for object identification, recognition, and even trajectory prediction. Optimised versions of such models, in particular the convolutional ones, are becoming increasingly common in resource-constrained edge-computing devices (e.g., sensors, drones), which typically rely on reduced memory footprint, low power budget and low-performance microprocessors. DNN models are prone to radiation-induced soft errors, and tackling their occurrence in resource-constrained devices is a mandatory and substantial challenge. While traditional replication-based soft error mitigation techniques will likely account for a reasonable performance penalty, hardware solutions are even more costly. To undertake this almost contradictory challenge, this work evaluates the efficiency of a lightweight software-based mitigation technique, called Register Allocation Technique (RAT), when applied to a convolutional neural network (CNN) model running on two commercial Arm microprocessors (i.e., Cortex-M4 and M7) under the effects of neutron radiation. Gathered results obtained from two neutron radiation campaigns suggest that RAT can reduce the number of critical faults in the CNN model running on both Arm Cortex-M microprocessors. Results also suggest that the SDC FIT rate of the RAT-hardened CNN model can be reduced in up to 83% with a runtime overhead of 32%

Soft error assessment of attitude estimation algorithms running on resource-constrained devices under neutron radiation

There is a growing incorporation of unmanned aerial vehicles (UAVs) within remote and urban environments due to their versatility and ability to access hard-to-reach and/or congested places. UAVs offer low-cost solutions for many applications, including healthcare (e.g., medical supplies delivery) and surveillance during public events, protests, or emergencies (e.g., nuclear accident). However, drone utilisation in urban areas often relies on strict regulations to ensure safe and responsible operation. UAVs are subject to radiation-induced soft errors, and identifying the most vulnerable software and hardware components to radiation exposure is a advisable task, which is difficult to undertake. An essential task to UAVs correct operation is attitude estimation. This paper assesses the soft error reliability of three attitude estimation algorithms running on two resource-constrained microprocessors under neutron radiation. Results suggest that the extended Kalman filter (EKF) algorithm provides the best mean work to failure result for critical fault events, which is about 3× more than the indirect Kalman filter (IKF) and 1.5× more w.r.t. the novel quaternion Kalman filter algorithm (NQKF).</p

Evaluation of the soft error assessment consistency of a JIT‐based virtual platform simulator

Soft error resilience has become an essential design metric in electronic computing systems as advanced technology nodes have become less robust to high-charged particle effects. Designers, therefore, should be able to assess this metric considering several software stack components running on top of commercial processors, early in the design phase. With this in mind, researchers are using virtual platform (VP) frameworks to assess this metric due to their flexibility and high simulation performance. In this regard, herein, this goal is achieved by analysing the soft error consistency of a just-in-time fault injection simulator (OVPsim-FIM) against fault injection campaigns conducted with event-driven simulators (i.e. more realistic and accurate platforms) considering single and multicore processor architectures. Reference single-core fault injection campaigns are performed on RTL descriptions of Arm Cortex-M0 and M3 processors, while gem5 simulator is used to multicore Arm Cortex-A9 scenarios. Campaigns consider different open-source and commercial compilers as well as real software stacks including FreeRTOS/Linux kernels and 52 applications. Results show that OVPsim-FIM is more than 1000× faster than cycle-accurate simulators and up to 312× faster than event-driven simulators, while preserving the soft error analysis accuracy (i.e. mismatch below to 10%) for single and multicore processors

Assessment of radiation-induced soft error on unmanned surface vehicles

The presence of Unmanned Surface Vehicles (USVs) is increasingly frequent on lakes and water reservoirs, performing tasks such as monitoring water quality or delivering goods across the water. However, the emergence of such autonomous vessels raises concerns in terms of safety for people sharing the same environment and the risk of collisions with fixed structures and floating bodies, including other vessels. Therefore, the detection of obstacles and its reliable operation become primary in USVs. This work explores the effects caused by neutron radiation on an object detection algorithm tailored for USVs. Results report 77 silent data corruption (SDC)-induced failures, showing that radiation-induced soft errors contribute to missed and false detection of respectively existing and non-existent objects. Furthermore, results suggest that object detection algorithms running with the multi-core strategy ( FITSDC rate of 34.3 at sea level and 308.6 at Lake Titicaca) exhibit a 16.4% greater resilience to SDCs compared to the single-core strategy.</p