42 citations · 62 across the 4 of their papers we have counts for
7 papers
Navigating Local Minima in Quantized Spiking Neural Networks
Jason K. Eshraghian, Corey Lammie, Mostafa Rahimi Azghadi +1
Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms. However, these networks face ch…
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures
Corey Lammie, Jason K. Eshraghian, Chenqi Li +4
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…
A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images
Alzayat Saleh, Issam H. Laradji, Corey Lammie +3
Health professionals extensively use Two- Dimensional (2D) Ultrasound (US) videos and images to visualize and measure internal organs for various purposes including evaluation of m…
Memristive Stochastic Computing for Deep Learning Parameter Optimization
Corey Lammie, Jason K. Eshraghian, Wei D. Lu +1
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 l…
Training Progressively Binarizing Deep Networks Using FPGAs
Corey Lammie, Wei Xiang, Mostafa Rahimi Azghadi
While hardware implementations of inference routines for Binarized Neural Networks (BNNs) are plentiful, current realizations of efficient BNN hardware training accelerators, suita…
Variation-aware Binarized Memristive Networks
Corey Lammie, Olga Krestinskaya, Alex James +1
The quantization of weights to binary states in Deep Neural Networks (DNNs) can replace resource-hungry multiply accumulate operations with simple accumulations. Such Binarized Neu…