McNair Scholar 2023 Jimena Jimenez
Jimena Jimenez is a junior at the University of Minnesota, Twin Cities, majoring in Electrical Engineering and minoring in Computer Science. Her research interests include novel computing architectures, biotechnology, and applied electromagnetics. Jimena plans on pursuing a Ph.D. in Electrical Engineering.
I aim to pursue a Ph.D in Electrical Engineering to nourish my research interests and forge my own path in investigating complex problems. I am committed to embracing interdisciplinary approaches to research, science communication, and education to foster innovative solutions and promote diverse perspectives.
Research project
Applying Stochastic Computing to Spiking Neural Networks for Ultra-Low Power Machine Learning Applications
Abstract: Networks of spiking neurons have emerged as the third-generation neural network model, employing spiking neurons to encode data through spikes, resembling biological neurons. Spiking neural networks (SNNs) offering promising potential for efficient computing, leveraging event-driven, parallel processing are being explored for modeling the dynamics of the human brain and implementing deep learning neural networks. This research proposes a novel method for implementing ultra-low-power SNNs using stochastic computing (SC). SC is a unique paradigm that operates on probabilities, encoded via streams of 0s and 1s. A major advantage of SC is the streamlining of multiplication with a single AND gate, simplifying the complex multiplication circuits needed for positional binary encoding. Two models for the digital representation of Izhikevich model spiking neurons on a field-programmable gate array (FPGA) are presented. One model uses classical positional representation, the other incorporates SC. The SC design reduces logic gates, enhancing scalability and lowers power consumption.
Faculty mentor
Marc Riedel is currently an Associate Professor of electrical and computer engineering with the University of Minnesota, Twin Cities, where he is a member of the Graduate Faculty of biomedical informatics and computational biology. He received his Ph.D. degree in electrical engineering from the California Institute of Technology (Caltech). Dr. Riedel’s research spans different disciplines ranging from digital circuit design, to algorithms, to mathematics, to synthetic biology. It tends to be inductive (as opposed to deductive) and conceptual (as opposed to applied). A recurring theme is building systems that compute in novel or unexpected ways with new and emerging technologies.