Bibliographic Details
| Title: |
PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials |
| Authors: |
Demirag, Yigit, Moro, Filippo, Dalgaty, Thomas, Navarro, Gabriele, Frenkel, Charlotte, Indiveri, Giacomo, Vianello, Elisa, Payvand, Melika |
| Publication Year: |
2021 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Emerging Technologies |
| More Details: |
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing. To endow these systems with additional flexibility and the ability to learn to solve specific tasks, it is important to develop appropriate on-chip learning mechanisms.Recently, a new class of three-factor spike-based learning rules have been proposed that can solve the temporal credit assignment problem and approximate the error back-propagation algorithm on complex tasks. However, the efficient implementation of these rules on hybrid CMOS/memristive architectures is still an open challenge. Here we present a new neuromorphic building block,called PCM-trace, which exploits the drift behavior of phase-change materials to implement long lasting eligibility traces, a critical ingredient of three-factor learning rules. We demonstrate how the proposed approach improves the area efficiency by >10X compared to existing solutions and demonstrates a techno-logically plausible learning algorithm supported by experimental data from device measurements
Comment: Typos are fixed |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2102.07260 |
| Accession Number: |
edsarx.2102.07260 |
| Database: |
arXiv |
