Drexel University 3 credits ECE
This course will cover the principles of neuromorphic computing. Topics will cover 1) fundamentals of spiking neural network (SNN); 2) supervised and unsupervised learning algorithms for SNN; 3) novel applications of SNN, including in vision and time series processing; 4) architectures for implementing SNN in hardware; 5) introduction to non-volatile memory technologies to implement synaptic processing in neuromorphic hardware; 6) software stacks for neuromorphic computing; and 7) design challenges in dependable neuromorphic computing. Software projects in this course will involve developing SNN models and training algorithms in PyTorch, a Python based machine learning framework. Hardware projects will be based on Verilog HDL and prototyped on FPGA. Familiarity with PyTorch and/or Verilog is required.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.