Corey Lammie

992 total citations
31 papers, 511 citations indexed

About

Corey Lammie is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Corey Lammie has authored 31 papers receiving a total of 511 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Electrical and Electronic Engineering, 11 papers in Cognitive Neuroscience and 9 papers in Artificial Intelligence. Recurrent topics in Corey Lammie's work include Advanced Memory and Neural Computing (26 papers), Ferroelectric and Negative Capacitance Devices (14 papers) and Neural dynamics and brain function (8 papers). Corey Lammie is often cited by papers focused on Advanced Memory and Neural Computing (26 papers), Ferroelectric and Negative Capacitance Devices (14 papers) and Neural dynamics and brain function (8 papers). Corey Lammie collaborates with scholars based in Australia, Switzerland and United States. Corey Lammie's co-authors include Mostafa Rahimi Azghadi, Jason K. Eshraghian, B. Linares-Barranco, Elisa Donati, Tara Julia Hamilton, Giacomo Indiveri, Melika Payvand, Alex Olsen, Wei Xiang and Wei Lü and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and IEEE Access.

In The Last Decade

Corey Lammie

29 papers receiving 502 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Corey Lammie Australia 12 362 142 138 113 46 31 511
Tzu-Hsiang Hsu Taiwan 13 650 1.8× 22 0.2× 121 0.9× 97 0.9× 106 2.3× 21 736
Gregor Lenz France 5 274 0.8× 134 0.9× 126 0.9× 73 0.6× 18 0.4× 12 361
Vasileios Ntinas Greece 13 522 1.4× 184 1.3× 74 0.5× 278 2.5× 6 0.1× 82 617
Arnab Neelim Mazumder United States 8 441 1.2× 127 0.9× 207 1.5× 132 1.2× 65 1.4× 19 578
Kuldeep Singh India 14 103 0.3× 262 1.8× 105 0.8× 21 0.2× 60 1.3× 50 535
Mohammad Bavandpour United States 9 401 1.1× 164 1.2× 138 1.0× 138 1.2× 23 0.5× 18 453
Zubaer Ibna Mannan South Korea 12 318 0.9× 213 1.5× 97 0.7× 93 0.8× 64 1.4× 15 508
Wenqin Huangfu United States 9 470 1.3× 27 0.2× 137 1.0× 141 1.2× 63 1.4× 14 564
De Ma China 14 405 1.1× 210 1.5× 197 1.4× 94 0.8× 47 1.0× 44 592

Countries citing papers authored by Corey Lammie

Since Specialization
Citations

This map shows the geographic impact of Corey Lammie's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Corey Lammie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Corey Lammie more than expected).

Fields of papers citing papers by Corey Lammie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Corey Lammie. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Corey Lammie. The network helps show where Corey Lammie may publish in the future.

Co-authorship network of co-authors of Corey Lammie

This figure shows the co-authorship network connecting the top 25 collaborators of Corey Lammie. A scholar is included among the top collaborators of Corey Lammie based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Corey Lammie. Corey Lammie is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lammie, Corey, Marina Zapater, Irem Boybat, et al.. (2025). LionHeart: A Layer-Based Mapping Framework for Heterogeneous Systems With Analog In-Memory Computing Tiles. IEEE Transactions on Emerging Topics in Computing. 13(4). 1383–1395. 1 indexed citations
2.
Lammie, Corey, et al.. (2025). The inherent adversarial robustness of analog in-memory computing. Nature Communications. 16(1). 1756–1756. 3 indexed citations
3.
Lammie, Corey, et al.. (2024). Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training. 1–5. 5 indexed citations
4.
Büchel, Julian, Benedikt Kersting, Corey Lammie, et al.. (2023). Exploiting the State Dependency of Conductance Variations in Memristive Devices for Accurate In-Memory Computing. IEEE Transactions on Electron Devices. 70(12). 6279–6285. 10 indexed citations
5.
Gallo, Manuel Le, Corey Lammie, Julian Büchel, et al.. (2023). Using the IBM analog in-memory hardware acceleration kit for neural network training and inference. SHILAP Revista de lepidopterología. 1(4). 27 indexed citations
6.
Büchel, Julian, Benedikt Kersting, Corey Lammie, et al.. (2023). Programming Weights to Analog In-Memory Computing Cores by Direct Minimization of the Matrix-Vector Multiplication Error. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 13(4). 1052–1061. 7 indexed citations
7.
8.
Azghadi, Mostafa Rahimi, et al.. (2023). Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey. Journal of Neural Engineering. 20(2). 21001–21001. 15 indexed citations
9.
Walters, Ben, Corey Lammie, Shuangming Yang, Mohan V. Jacob, & Mostafa Rahimi Azghadi. (2023). Unsupervised character recognition with graphene memristive synapses. Neural Computing and Applications. 36(4). 1569–1584. 4 indexed citations
10.
Lammie, Corey, et al.. (2023). Simulation of memristive crossbar arrays for seizure detection and prediction using parallel Convolutional Neural Networks. Software Impacts. 15. 100473–100473. 4 indexed citations
11.
Lammie, Corey, et al.. (2022). Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation. arXiv (Cornell University). 1–4. 2 indexed citations
12.
Lammie, Corey, Wei Xiang, B. Linares-Barranco, & Mostafa Rahimi Azghadi. (2022). MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems. Neurocomputing. 485. 124–133. 29 indexed citations
13.
Lammie, Corey, Wei Xiang, & Mostafa Rahimi Azghadi. (2021). Modeling and simulating in-memory memristive deep learning systems: An overview of current efforts. Array. 13. 100116–100116. 11 indexed citations
14.
Lammie, Corey, Mostafa Rahimi Azghadi, & Daniele Ielmini. (2021). Empirical metal-oxide RRAM device endurance and retention model for deep learning simulations. Semiconductor Science and Technology. 36(6). 65003–65003. 11 indexed citations
15.
Lammie, Corey, Jason K. Eshraghian, Wei Lü, & Mostafa Rahimi Azghadi. (2021). Memristive Stochastic Computing for Deep Learning Parameter Optimization. IEEE Transactions on Circuits & Systems II Express Briefs. 68(5). 1650–1654. 30 indexed citations
16.
Azghadi, Mostafa Rahimi, Corey Lammie, Jason K. Eshraghian, et al.. (2020). Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications. Repository for Publications and Research Data (ETH Zurich). 135 indexed citations
17.
Lammie, Corey & Mostafa Rahimi Azghadi. (2020). MemTorch: A Simulation Framework for Deep Memristive Cross-Bar Architectures. 1–5. 23 indexed citations
18.
Lammie, Corey & Mostafa Rahimi Azghadi. (2020). Live Demonstration: Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge. 1–1. 3 indexed citations
19.
Lammie, Corey, et al.. (2019). Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge. IEEE Access. 7. 51171–51184. 62 indexed citations
20.
Lammie, Corey, Tara Julia Hamilton, André van Schaik, & Mostafa Rahimi Azghadi. (2018). Efficient FPGA Implementations of Pair and Triplet-Based STDP for Neuromorphic Architectures. IEEE Transactions on Circuits and Systems I Regular Papers. 66(4). 1558–1570. 42 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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