Performance and Comparisons of STDP based and Non-STDP based Memristive Neural Networks on Hardware
Zhiri Tang

TL;DR
This paper compares memristive neural networks based on STDP and non-STDP mechanisms, demonstrating that non-STDP approaches offer similar accuracy but improved hardware efficiency and processing speed.
Contribution
It introduces a non-STDP based memristive neural network that maintains high performance while reducing hardware complexity and increasing processing speed.
Findings
Non-STDP MNNs achieve comparable pattern recognition accuracy to STDP MNNs.
Non-STDP MNNs require fewer hardware resources and have higher processing speeds.
Non-STDP MNNs exhibit better hardware compatibility for engineering applications.
Abstract
With the development of research on memristor, memristive neural networks (MNNs) have become a hot research topic recently. Because memristor can mimic the spike timing-dependent plasticity (STDP), the research on STDP based MNNs is rapidly increasing. However, although state-of-the-art works on STDP based MNNs have many applications such as pattern recognition, STDP mechanism brings relatively complex hardware framework and low processing speed, which block MNNs' hardware realization. A non-STDP based unsupervised MNN is constructed in this paper. Through the comparison with STDP method on the basis of two common structures including feedforward and crossbar, non-STDP based MNNs not only remain the same advantages as STDP based MNNs including high accuracy and convergence speed in pattern recognition, but also better hardware performance as few hardware resources and higher processing…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Memory and Neural Computing · CCD and CMOS Imaging Sensors · Neuroscience and Neural Engineering
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
