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Tergel Molom-Ochir Ph.D. Candidate |
Biography
I am a Ph.D. candidate in Electrical and Computer Engineering at Duke University, advised by Dr. Yiran Chen and Dr. Hai "Helen" Li in the Center for Computational Evolutionary Intelligence (CEI). I also work as a Research Associate at Hewlett Packard Labs on the Emerging Accelerators team.
My research focuses on hardware architectures for AI—spanning digital content-addressable memories (CAMs), analog in-memory computing, and neuromorphic circuits.
Previously, I worked with Dr. Qiangfei Xia at UMass Amherst on memristive devices, and with Dr. Yingyan Lin at Rice University on efficient deep learning.
News
[Dec 2025] Paper on CAM-based DNA classification accepted at HPCA 2025!
[Oct 2025] Patent filed at Hewlett Packard Enterprise for In-Memory MCTS Accelerator.
[2025] Paper accepted at ISCAS 2025: MonoSparse-CAM.
[2025] CAM Survey published in IEEE TCAS-I.
[May 2025] Started Research Associate position at HPE Labs.
[Aug 2023] Joined Duke University CEI Lab.
Research Highlights
I investigate associative memory across three computational substrates:
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Digital AM CAM-based Architectures CAMformer, NP-CAM, MonoSparse-CAM, CAM Survey |
Analog AM Memristive Crossbars & In-Memory Computing HPE Labs |
Biological AM Spiking & Neuromorphic Circuits Neuromorphic Hardware |
Selected Publications
T. Molom-Ochir, B. Taylor, H. Li, Y. Chen. "Advancements in Content-Addressable Memory (CAM) Circuits: State-of-the-Art, Applications, and Future Directions in the AI Domain." IEEE TCAS-I, 2025. [PDF]
B. Morris, T. Molom-Ochir, et al. "NP-CAM: Efficient and Scalable DNA Classification using NoC-Partitioned CAM Architectures." HPCA 2025. [PDF]
T. Molom-Ochir, B. Taylor, H. Li, Y. Chen. "MonoSparse-CAM: Efficient Tree Model Processing via Monotonicity and Sparsity in CAMs." ISCAS 2025. [PDF]
T. Molom-Ochir, et al. "CAMformer: Associative Memory is All You Need." arXiv preprint, 2025. [PDF] [Code]
