TY  - CONF
AU  - Neftci, Emre
TI  - Pre-training and Meta-learning for Memristor Crossbar Arrays
M1  - FZJ-2024-02777
PY  - 2023
AB  - Memristive crossbar arrays show promise as non-von Neumann computing technologies, bringing sophisticated neural network processing to the edge and facilitating real-world online learning. However, their deployment for real-world learning problems faces challenges such as non-linearities in conductance updates, variations during operation, fabrication mismatch, conductance drift, and the realities of gradient descent training.This talk will present methods to pre-train neural networks to be largely insensitive to these non-idealities during learning tasks. These methods rely on a phenomenological model of the device, obtainable experimentally, and bi-level optimization. We showcase this effect through meta-learning and a differentiable model of conductance updates on few-shot learning tasks. Since pre-training is a necessary procedure for any online learning scenario at the edge, our results may pave the way for real-world applications of memristive devices without significant adaptation overhead.Furthermore, by considering the programming of memristive devices as a learning problem in its own right, we demonstrate that the developed methods can accelerate existing write-verify techniques.
T2  - Neuronics Conference
CY  - 21 Feb 2024 - 23 Feb 2024, València (Spain)
Y2  - 21 Feb 2024 - 23 Feb 2024
M2  - València, Spain
LB  - PUB:(DE-HGF)6
DO  - DOI:10.29363/nanoge.neuronics.2024.027
UR  - https://juser.fz-juelich.de/record/1025209
ER  -