211 citations · 527 across the 17 of their papers we have counts for
17 papers
A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing
Elena Ferro, Athanasios Vasilopoulos, Corey Lammie +4
Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is requ…
Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training
Corey Lammie, Athanasios Vasilopoulos, Julian Büchel +4
Analog-Based In-Memory Computing (AIMC) inference accelerators can be used to efficiently execute Deep Neural Network (DNN) inference workloads. However, to mitigate accuracy losse…
LionHeart: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles
Corey Lammie, Yuxuan Wang, Flavio Ponzina +7
When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learnin…
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Manuel Le Gallo, Corey Lammie, Julian Buechel +8
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and n…
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
Hadjer Benmeziane, Corey Lammie, Irem Boybat +9
The advancement of Deep Learning (DL) is driven by efficient Deep Neural Network (DNN) design and new hardware accelerators. Current DNN design is primarily tailored for general-pu…
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks
Chenqi Li, Corey Lammie, Xuening Dong +3
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware imple…
Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation
Tim Zhang, Corey Lammie, Mostafa Rahimi Azghadi +3
Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities. The development of customized hardware implementin…
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…
Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction
Corey Lammie, Wei Xiang, Mostafa Rahimi Azghadi
The unpredictability of seizures continues to distress many people with drug-resistant epilepsy. On account of recent technological advances, considerable efforts have been made us…
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian +4
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorit…
MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems
Corey Lammie, Wei Xiang, Bernabé Linares-Barranco +1
Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using…
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…
Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL
Corey Lammie, Wei Xiang, Mostafa Rahimi Azghadi
Recent technological advances have proliferated the available computing power, memory, and speed of modern Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Fi…