Analog Implementation of a Spiking Neuron with Memristive Synapses for Deep Learning Processing
-
Published:2024-06-29
Issue:13
Volume:12
Page:2025
-
ISSN:2227-7390
-
Container-title:Mathematics
-
language:en
-
Short-container-title:Mathematics
Author:
Ramirez-Morales Royce R.1ORCID, Ponce-Ponce Victor H.1ORCID, Molina-Lozano Herón1ORCID, Sossa-Azuela Humberto1ORCID, Islas-García Oscar1, Rubio-Espino Elsa1ORCID
Affiliation:
1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Alcaldía GAM, Ciudad de México 07700, Mexico
Abstract
Analog neuromorphic prototyping is essential for designing and testing spiking neuron models that use memristive devices as synapses. These prototypes can have various circuit configurations, implying different response behaviors that custom silicon designs lack. The prototype’s behavior results can be optimized for a specific foundry node, which can be used to produce a customized on-chip parallel deep neural network. Spiking neurons mimic how the biological neurons in the brain communicate through electrical potentials. Doing so enables more powerful and efficient functionality than traditional artificial neural networks that run on von Neumann computers or graphic processing unit-based platforms. Therefore, on-chip parallel deep neural network technology can accelerate deep learning processing, aiming to exploit the brain’s unique features of asynchronous and event-driven processing by leveraging the neuromorphic hardware’s inherent parallelism and analog computation capabilities. This paper presents the design and implementation of a leaky integrate-and-fire (LIF) neuron prototype implemented with commercially available components on a PCB board. The simulations conducted in LTSpice agree well with the electrical test measurements. The results demonstrate that this design can be used to interconnect many boards to build layers of physical spiking neurons, with spike-timing-dependent plasticity as the primary learning algorithm, contributing to the realization of experiments in the early stage of adopting analog neuromorphic computing.
Funder
Secretaría de Investigación y Posgrado del Instituto Politécnico Nacional Comisión de Operación y Fomento de Actividades Académicas and Consejo Nacional de Humanidades Ciencia y Tecnología
Reference46 articles.
1. Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020). Language Models are Few-Shot Learners. arXiv. 2. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv. 3. Luccioni, A.S., Viguier, S., and Ligozat, A.L. (2022). Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. arXiv. 4. Mead, C., and Ismail, M. (1989). Analog VLSI Implementation of Neural Systems, Springer. 5. Canales-Verdial, J.I., Wagner, J.R., Schmucker, L.A., Wetzel, M., Proctor, P., Carson, M., Meng, J., Withers, N.J., Harris, C.T., and Nogan, J.J. (2024). Energy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications. Sensors, 24.
|
|