Publikation
Fault-Tolerant Character Recognition in Neuromorphic Systems Using RRAM Crossbar Arrays
Fatemeh Shirinzadeh; Abhoy Kole; Kamalika Datta; Saeideh Shirinzadeh; Rolf Drechsler
In: IEEE Nordic Circuits and Systems Conference (NorCAS). IEEE Nordic Circuits and Systems Conference (NorCAS-2025), October 21-23, Riga, Lithuania, IEEE, 2025.
Zusammenfassung
Resistive Random-Access Memory (RRAM) crossbar
arrays provide a high-density, low-power platform for neuromorphic
computing. In this work, we implement an RRAM-based architecture
for alphabet recognition using the EMNIST dataset, where all 26
English letters are represented as 28×28 binary images. Beyond ideal
conditions, we study the impact of hardware imperfections, including
stuck-at faults, random bit flips, and process variations, on recognition
performance. To improve resilience, we evaluate two fault tolerance
strategies: Triple Modular Redundancy (TMR) and Algorithm-Based
Fault Tolerance (ABFT). TMR delivers strong reliability by masking
faults through replication, while ABFT efficiently detects and corrects
at a lower storage overhead, but at a higher computational cost.
Our results demonstrate that RRAM crossbars combined with
lightweight fault tolerance provide accurate, energy-efficient, and
resilient neuromorphic computing, highlig
