Research Area:  Machine Learning

Abstract:

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.

Keywords:  
Reliability
Software
Registers
Radio access technologies
Computational modeling
Redundancy
Performance evaluation

Author(s) Name:  Geancarlo Abich; Jonas Gava; Rafael Garibotti

Publisher name:  IEEE

DOI:  10.1109/TCSI.2021.3097981

Volume Information:   Volume: 68

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9493729/