Single-Event Upset Analysis of a Systolic Array based Deep Neural Network Accelerator

Autor:	Jonckers, Naïn, Vinck, Toon, Dekkers, Gert, Karsmakers, Peter, Prinzie, Jeffrey
Rok vydání:	2024
Předmět:	Computer Science - Hardware Architecture Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Deep Neural Network (DNN) accelerators are extensively used to improve the computational efficiency of DNNs, but are prone to faults through Single-Event Upsets (SEUs). In this work, we present an in-depth analysis of the impact of SEUs on a Systolic Array (SA) based DNN accelerator. A fault injection campaign is performed through a Register-Transfer Level (RTL) based simulation environment to improve the observability of each hardware block, including the SA itself as well as the post-processing pipeline. From this analysis, we present the sensitivity, independent of a DNN model architecture, for various flip-flop groups both in terms of fault propagation probability and fault magnitude. This allows us to draw detailed conclusions and determine optimal mitigation strategies. Comment: This work has been submitted to RADECS 2024 for possible publication
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.15381 Zobrazit plný text záznamu View this record from Arxiv