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NEURON Lab

NEURON Lab

NEURON: Nexus for Efficient Utilization of Robust and Optimized Neuromorphic Computing

lab's logo is a brain with circuit board styling and neuron text
At NEURON, we bridge the gap between theoretical machine learning and real-world deployment. Our research is centered on Hyperdimensional Computing (HDC), a brain-inspired computing paradigm that provides high efficiency, robustness, and interpretability compared to traditional deep learning models. Our work follows a vertically integrated approach, spanning the entire stack from high-level algorithmic innovation down to physical hardware implementation. In addition to machine learning research, we are also focused on inclusive engineering education. We have 3 core research threads at NEURON:
  • Applied Machine Learning & Hyperdimensional Computing (HDC)
  • Hardware-Software Co-Design & Emerging Computing Technology
  • Inclusive Engineering Education

Sections Menu

Hyperdimensional Computing

digital outline of a brain hooked into a circuit board

At NEURON, we investigate the transformative potential of Hyperdimensional Computing (HDC), a robust, brain-inspired paradigm that leverages high-dimensional vector spaces to achieve remarkable computational efficiency and interpretability. Our research focuses on advancing the mathematical foundations and algorithmic frameworks of HDC, exploring how complex information can be encoded, processed, and retrieved through simple algebraic operations on high-dimensional vectors. By moving away from the resource-intensive matrix multiplications of traditional deep learning toward a framework defined by rapid learning and efficient bitwise operations, we are developing a new class of intelligent systems capable of performing robust, energy-efficient inference for real-world applications.

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Emerging Computing Technology

PIM

With the end of exponential transistor scaling, computing power is no longer limited by how small we can build, but by energy and thermal constraints. If we continue on our current trajectory without revolutionary change, the energy consumption of global computing infrastructure could become ecologically and economically untenable. To maintain progress, we must "do more with less"—extracting higher performance from each watt of power by reimagining the fundamental relationship between data and hardware. Emerging computing technologies like PIM, neuromorphic architectures, and advanced 3D integration are critical research paths to continue improving computational efficiency in the post-Moore era, as they allow us to overcome the physical limitations of scaling by fundamentally redefining the relationship between data processing and memory.

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Inclusive Engineering Education

The Flipped Classroom: Before (out of class) - students prepare to participate in the class activities, During (in class) - students practice applying key concepts with feedback, After (out of class) - students check their understanding and extend their learning

The flipped classroom has proven highly successful by fundamentally reordering the learning experience to prioritize active engagement over passive consumption. By shifting the delivery of foundational content—typically via recorded lectures or readings—to the pre-class phase, it clears the classroom of traditional "sage-on-the-stage" lectures. This creates essential space for high-impact, collaborative activities, such as problem-solving sessions, peer discussions, and laboratory work, where students can apply knowledge while under the guidance of the instructor. This model significantly increases student agency and allows for a more personalized learning pace, as students can revisit complex concepts as needed. Furthermore, by providing students with immediate feedback and real-time support from their instructor and peers during the more challenging application phase, the flipped approach fosters deeper conceptual understanding and helps narrow the gap for learners who might otherwise struggle when attempting complex assignments independently.

The flipped classroom has been proven to increase student success in mulitple domains. However, there is not extensive research on the application of a flipped classroom at Universities serving underrepresented students. In our lab, we are employing a flipped classroom at CSUSM in engineering courses to research the small changes and tweaks to the flipped classroom framework that helps our students become more successful in their learning.

Image is originally from: UT Austin

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Conference Publications

  1. Mehjabeen Tasnim, Justin Morris, and Sreedevi Gutta. "Lightweight and Generalizable Glioma Grading Using Hyperdimensional Computing." International Conference on Software Engineering of Emerging Technology. Cham: Springer Nature Switzerland, 2025.
  2. Shyam Yathirajam, Arash Peighambari, Nikil Balaji, Owen Man, Hamed Nademi, Sreedevi Gutta, Justin Morris, and Ali Ahmadinia. "Improved Subsynchronous Frequency Oscillations Detection in Wind Farms Using AI-based Fourier Transformation and Advanced Metrics." 2025 IEEE Green Technologies Conference (GreenTech). IEEE, 2025.
  3. Fatemeh Asgarinejad, Justin Morris, Tajana Rosing, and Baris Aksanli. "Visionhd: Towards efficient and privacy-preserved hyperdimensional computing for image data." Proceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design. 2024.
  4. Weihong Xu, Saransh Gupta, Justin Morris, Xincheng Shen, Mohsen Imani, Baris Aksanli, and Tajana Rosing. "Tri-HD: Energy-efficient on-chip learning with in-memory hyperdimensional computing." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 44.2 (2024): 525-539.
  5. Fatemeh Asgarinejad, Justin Morris, Tajana Rosing, and Baris Aksanli. "Pioneer: Highly efficient and accurate hyperdimensional computing using learned projection." 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 2024.
  6. Shyam Yathirajam, Arash Peighambari, Ruben Roberts, Hamed Nademi, Sreedevi Gutta, Justin Morris, and Ali Ahmadinia. "Windfarm forced oscillation detection using hyperdimensional computing." 2023 IEEE International Conference on Big Data (BigData). IEEE, 2023.
  7. Mohsen Imani, Yeseong Kim, Behnam Khaleghi, Justin Morris, Haleh Alimohamadi, Farhad Imani, and Hugo Latapie. "Hierarchical, distributed and brain-inspired learning for internet of things systems." 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS). IEEE, 2023.
  8. Tinaqi Zhang, Sahand Salamat, Behnam Khaleghi, Justin Morris, Baris Aksanli, and Tajana Simunic Rosing. "HD2FPGA: Automated framework for accelerating hyperdimensional computing on FPGAs." 2023 24th International Symposium on Quality Electronic Design (ISQED). IEEE, 2023.
  9. Behnam Khaleghi, Jaeyoung Kang, Hanyang Xu, Justin Morris, Tajana Rosing ”GENERIC: Highly Efficient Learning Engine on Edge using Hyperdimensional Computing”, Design Automation Conference (DAC), 2022.
  10. Justin Morris, Hin Wai Lui, Kenneth Stewart, Behnam Khaleghi, Anthony Thomas, Thiago Marback, Baris Aksanli, Emre Neftci, and Tajana Rosing, ”HyperSpike: HyperDimensional Computing for More Efficient and Robust Spiking Neural Networks,” in Design, Automation Test in Europe Conference Exhibition (DATE), 2022.
  11. Yilun Hao, Saransh Gupta, Justin Morris, Behnam Khaleghi, Baris Aksanli, and Tajana Rosing, ”Stochastic-HD: Leveraging Stochastic Computing on Hyper-Dimensional Computing,” in IEEE 39th International Conference on Computer Design (ICCD), 2021.
  12. Justin Morris, Si Thu Kaung Set, Gadi Rosen, Mohsen Imani, Baris Aksanli, and Tajana Rosing, ”AdaptBit-HD: Adaptive Model Bitwidth for Hyperdimensional Computing,” in IEEE 39th International Conference on Computer Design (ICCD), 2021.
  13. Alice Sokolova, Mohsen Imani, Andrew Huang, Ricardo Garcia, Justin Morris, Tajana Rosing, and Baris Aksanli, ”MACcelerator: Approximate Arithmetic Unit for Computational Acceleration,” in 22nd International Symposium on Quality Electronic Design (ISQED) 2021.
  14. Behnam Khaleghi, Hanyang Xu, Justin Morris, and Tajana Simuni ˇ c Rosing, ”tiny-HD: Ultra- ´Efficient Hyperdimensional Computing Engine for IoT Applications,” in Design, Automation Test in Europe Conference Exhibition (DATE), 2021.
  15. Justin Morris, Kazim Ergun, Behnam Khaleghi, Mohsen Imani, Baris Aksanli, and Tajana Rosing, ”Hydrea: Towards more robust and efficient machine learning systems with hyperdimensional computing,” in Design, Automation Test in Europe Conference Exhibition (DATE), 2021.
  16. Yunhui Guo, Mohsen Imani, Jaeyoung Kang, Sahand Salamat, Justin Morris, Baris Aksanli, Yeseong Kim, and Tajana Rosing, ”HyperRec: Efficient Recommender Systems with Hyperdimensional Computing,” in 26th Asia and South Pacific Design Automation Conference (ASP-DAC), 2021.
  17. Saransh Gupta, Justin Morris, Mohsen Imani, Ranganathan Ramkumar, Jeffrey Yu, Aniket Tiwari, Baris Aksanli, and Tajana Simunic Rosing, ”THRIFTY: Training with Hyperdimensional Computing across Flash Hierarchy,” in IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2020.
  18. Justin Morris, Yilun Hao, Saransh Gupta, Ranganathan Ramkumar, Jeffrey Yu, Mohsen Imani, Baris Aksanli, and Tajana Simunic Rosing, ”Multi-label HD Classification in 3D Flash,” in Proceedings of IFIP/IEEE International Conference on VLSI and System-on-Chip (VLSI-SoC), 2020.
  19. Mohsen Imani, Justin Morris, Samuel Bosch, Helen Shu, Giovanni De Micheli, and Tajana Rosing, ”Adapthd: Adaptive efficient training for brain-inspired hyperdimensional computing,” in IEEE Biomedical Circuits and Systems Conference (BioCAS), 2019.
  20. Justin Morris, Mohsen Imani, Samuel Bosch, Anthony Thomas, Helen Shu, and Tajana Rosing, ”CompHD: Efficient hyperdimensional computing using model compression,” in IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2019.
  21. Mohsen Imani, Justin Morris, John Messerly, Helen Shu, Yaobang Deng, and Tajana Rosing, ”Bric: Locality-based encoding for energy-efficient brain-inspired hyperdimensional computing,” in Proceedings of the 56th Annual Design Automation Conference (DAC), 2019.
  22. Mohsen Imani, Tarek Nassar, Justin Morris, and Tajana Rosing, ”DNA Sequencing using Braininspired Hyperdimensional Computing,” in GOMACTech Conference, 2019.

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Journal Publications

  1. Fatemeh Asgarinejad, Xiaofan Yu, Danlin Jiang, Justin Morris, Tajana Rosing, and Baris Aksanli. "Enhanced Noise-Resilient Pressure Mat System Based on Hyperdimensional Computing." Sensors 24.3 (2024): 1014.
  2. Rebecca Fielding-Miller, Smruthi Karthikeyan, Tommi Gaines, Richard S. Garfein, Rodolfo A. Salido, Victor J. Cantu, Laura Kohn et al. "Safer at school early alert: an observational study of wastewater and surface monitoring to detect COVID-19 in elementary schools." The Lancet Regional Health–Americas 19 (2023).  
  3. Justin Morris, Yilun Hao, Saranash Gupta, Behnam Khaleghi, Baris Aksanli, Tajana Rosing. ”Stochastic-HD: Leveraging Stochastic Computing on the Hyper-Dimensional Computing Pipeline”, Frontiers in Neuroscience, 2022.
  4. George Armstrong, Cameron Martino, Justin Morris, Behnam Khaleghi, Jaeyoung Kang, Jeff DeReus, Qiyun Zhu et al. ”Swapping Metagenomics Preprocessing Pipeline Components Offers Speed and Sensitivity Increases.” Msystems (2022): e01378-21.
  5. Justin Morris, Kazim Ergun, Behnam Khaleghi, Mohsen Imani, Baris Aksanli, and Tajana Rosing, ”HyDREA: Utilizing Hyperdimensional Computing For A More Robust and Efficient Machine Learning System.” ACM Transactions on Embedded Computing Systems (TECS), 2022.
  6. Justin Morris, Yilun Hao, Roshan Fernando, Mohsen Imani, Baris Aksanli, and Tajana Rosing, ”Locality-based Encoder and Model Quantization for Efficient Hyper-Dimensional Computing,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2021.
  7. Rebecca K. Fielding-Miller, Smruthi Karthikeyan, Tommi Gaines, Richard S. Garfein, Rodolfo A. Salido, Victor Cantu, Laura Kohn et al. ”Wastewater and surface monitoring to detect COVID-19 in elementary school settings: The Safer at School Early Alert project.” Medrxiv (2021).
  8. Mohsen Imani, Justin Morris, Helen Shu, Shou Li, and Tajana Rosing, ”Efficient associative search in brain-inspired hyperdimensional computing,” in IEEE Design Test 37, no. 1 2019 28-35.

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Alumni

Name - First Position After Graduation

From University of California San Diego

  • Yilun Hao (B.S.) - M.S. Stanford
  • Gadi Rosen (B.S.) - M.S. UC Berkeley
  • Si Thu Kaung Set (B.S.) - M.S. UC San Diego
  • Roshan Fernando (B.S.) - Contrary
  • Helen Shu (B.S.) - Palo Alto Networks
  • Fatemeh Asgarinejad (PhD) - Assistant Professor UC Riverside
  • Rishikanth Chandrasekaran (PhD) - Ebay

From California State University San Marcos

  • Mehjabeen Khan (B.S.)
  • Owen Man (M.S.) - Microsoft
  • Christopher Rubin (B.S.) - BAE Systems
  • Valeria Romero - Kiewit
  • Arash Peighambari (M.S.) - PhD Texas A&M University
  • Yiquan Cao (M.S.)
  • Shyam Yathirajam (M.S.) - Intuit
  • Ruben Roberts (B.S.) - M.S. UC San Diego

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Support

NEURON is funded by multiple agencies including: the National Science Foundation (NSF), the National Institues of Health (NIH), California Job's First, Internal CSUSM funding, SKALE, etc.

Send inquiries about how you can also support NEURON

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