Cory Merkel Headshot

Cory Merkel

Associate Professor

Department of Computer Engineering
Kate Gleason College of Engineering
cemeec@rit.edu
585-475-4083
Office Location
GLE-3461

Cory Merkel

Associate Professor

Department of Computer Engineering
Kate Gleason College of Engineering

Education

BS, MS, Ph.D., Rochester Institute of Technology

Bio

Dr. Cory Merkel joined the RIT computer engineering department in 2018. He earned his BS and MS degrees in computer engineering (2011) and a Ph.D. in microsystems engineering (2015), all from RIT. From 2016 to 2018, Dr. Merkel was a research electronics engineer with the Information Directorate, Air Force Research Lab. His current research focuses on mapping of AI algorithms, primarily artificial neural networks, to mixed-signal hardware and the design of brain-inspired computing systems using emerging technologies such as memristors. He has published his work in several peer-reviewed conferences, journals, and books, and is also engaged in a number of STEM outreach activities. For more information, see Dr. Merkel’s research website www.rit.edu/brainlab.

cemeec@rit.edu
585-475-4083
Personal Links
Personal Website

Select Scholarship

Journal Paper
Jha, Alexander Jones, Andrew Rush, Cory Merkel, Eric Herrmann, Ajey P. Jacobs, Clare Thiem, Rashmi. "A Neuromorphic SLAM Architecture Using Gated-memristive Synapses." Neurocomputing 381. (2020): 89-104. Web.
Published Conference Proceedings
Fountain, Andrew and Cory Merkel. "Energy Constraints Improve Liquid State Machine Performance." Proceedings of the International Conference on Neuromorphic Systems. Ed. Catherine Schuman. N/A, United States: n.p., 2020. Web.
Merkel, Cory. "Exploring Energy-Accuracy Tradeoffs in AI Hardware." Proceedings of the International Green and Sustainable Computing Conference. Ed. Behrooz Shirazi. N/A, United States: n.p., 2020. Web.
Jones, Alex, et al. "A Segmented Attractor Network for Neuromorphic Associative Learning." Proceedings of the International Conference on Neuromorphic Systems. Ed. N/A. Knoxville, Tennessee: n.p., Web.
Merkel, Cory and Animesh Nikam. "A Low-Power Domino Logic Architecture for Memristor-Based Neuromorphic Computing." Proceedings of the International Conference on Neuromorphic Systems. Ed. N/A. Knoxville, Tennessee: n.p., Web.
Langroudi, Hamed, et al. "Exploiting Randomness in Deep Learning Algorithms." Proceedings of the International Joint Conference on Neural Networks. Ed. N/A. Budapest, Hungary: n.p., Web.
Merkel, Cory. "Current-Mode Memristor Crossbars for Neuromorphic Computing." Proceedings of the Neuro Inspired Computational Elements. Ed. N/A. Albany, New York: n.p., Web.

Currently Teaching

CMPE-260
Digital System Design II
4 Credits
This course presents modern approaches to the design, modeling and testing of digital system. Topics covered are: VHDL and Verilog HDL as hardware description languages (HDLs), simulation techniques, design synthesis, verification methods, and implementation with field programmable gate arrays (FPGAs). Combinational and both the synchronous and asynchronous sequential circuits are studied. Testing and design for testability techniques are emphasized and fault tolerant and fail safe design concepts are introduced. Laboratory projects that enable students gain hands-on experience are required. The projects include complete design flow: design of the system, modeling using HDLs, simulation, synthesis and verification.
CMPE-677
Machine Intelligence
3 Credits
Machine intelligence teaches devices how to learn a task without explicitly programming them how to do it. Example applications include voice recognition, automatic route planning, recommender systems, medical diagnosis, robot control, and even Web searches. This course covers an overview of machine learning topics with a computer engineering influence. Includes Matlab programming. Course topics include unsupervised and supervised methods, regression vs. classification, principal component analysis vs. manifold learning, feature selection and normalization, and multiple classification methods (logistic regression, regression trees, Bayes nets, support vector machines, artificial neutral networks, sparse representations, and deep learning).
CMPE-789
Special Topics
3 Credits
Graduate level topics and subject areas that are not among the courses typically offered are provided under the title of Special Topics. Such courses are offered in a normal format; that is, regularly scheduled class sessions with an instructor.
MATH-790
Research & Thesis
0 - 9 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

In the News

More News  

Featured Work