Search Results for author: Wei D. Lu

Found 8 papers, 3 papers with code

Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays

no code implementations26 Jun 2022 Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi, Wei D. Lu, Sung-Mo Kang

We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs).

Navigating Local Minima in Quantized Spiking Neural Networks

1 code implementation15 Feb 2022 Jason K. Eshraghian, Corey Lammie, Mostafa Rahimi Azghadi, Wei D. Lu

Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms.


The fine line between dead neurons and sparsity in binarized spiking neural networks

1 code implementation28 Jan 2022 Jason K. Eshraghian, Wei D. Lu

Spiking neural networks can compensate for quantization error by encoding information either in the temporal domain, or by processing discretized quantities in hidden states of higher precision.


Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures

no code implementations18 Jan 2022 Corey Lammie, Jason K. Eshraghian, Chenqi Li, Amirali Amirsoleimani, Roman Genov, Wei D. Lu, Mostafa Rahimi Azghadi

The impact of device and circuit-level effects in mixed-signal Resistive Random Access Memory (RRAM) accelerators typically manifest as performance degradation of Deep Learning (DL) algorithms, but the degree of impact varies based on algorithmic features.


Hierarchical Architectures in Reservoir Computing Systems

no code implementations14 May 2021 John Moon, Wei D. Lu

Analogous to deep neural networks, stacking sub-reservoirs in series is an efficient way to enhance the nonlinearity of data transformation to high-dimensional space and expand the diversity of temporal information captured by the reservoir.

Memristive Stochastic Computing for Deep Learning Parameter Optimization

no code implementations11 Mar 2021 Corey Lammie, Jason K. Eshraghian, Wei D. Lu, Mostafa Rahimi Azghadi

Stochastic Computing (SC) is a computing paradigm that allows for the low-cost and low-power computation of various arithmetic operations using stochastic bit streams and digital logic.


Field-Programmable Crossbar Array (FPCA) for Reconfigurable Computing

no code implementations9 Dec 2016 Mohammed A. Zidan, YeonJoo Jeong, Jong Hong Shin, Chao Du, Zhengya Zhang, Wei D. Lu

The proposed computing architecture is based on a uniform, physical, resistive, memory-centric fabric that can be optimally reconfigured and utilized to perform different computing and data storage tasks in a massively parallel approach.


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