A Novel Computing Paradigm for MobileNetV3 using Memristor
CoRR(2024)
摘要
The advancement in the field of machine learning is inextricably linked with
the concurrent progress in domain-specific hardware accelerators such as GPUs
and TPUs. However, the rapidly growing computational demands necessitated by
larger models and increased data have become a primary bottleneck in further
advancing machine learning, especially in mobile and edge devices. Currently,
the neuromorphic computing paradigm based on memristors presents a promising
solution. In this study, we introduce a memristor-based MobileNetV3 neural
network computing paradigm and provide an end-to-end framework for validation.
The results demonstrate that this computing paradigm achieves over 90%
accuracy on the CIFAR-10 dataset while saving inference time and reducing
energy consumption. With the successful development and verification of
MobileNetV3, the potential for realizing more memristor-based neural networks
using this computing paradigm and open-source framework has significantly
increased. This progress sets a groundbreaking pathway for future deployment
initiatives.
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