Jeremy Holleman, Ph.D.

Education:

• Ph.D. University of Washington, 2009
• M.S. University of Washington, 2006
• B.S. Georgia Institute of Technology, 1997

Research:

• Low-power Analog, Mixed-signal, and RF Circuits
• Bio-medical Interface Design
• Neuromorphic Computation
• Machine Learning Hardware
• Resource-constrained Embedded Systems

Selected Publications:

• M. Judy, N.C. Poore, P. Liu, T. Yang, C. Britton, D. S. Bolme, A. K. Mikkilineni, and J. Holleman, “A Digitally Interfaced Analog Correlation Filter System for Object Tracking Applications.” IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 65, No. 9, pp. 2764–2773, 2018.
• Hasan, M. Munir, and Jeremy Holleman. ”Implementation of Linear Discriminant Classifier in 130nm Silicon Process.” In Circuits and Systems (ISCAS), 2018 IEEE International Symposium on, pp. 1-5. IEEE, 2018.
• T. Yang, J. Holleman, “An Ultra-Low-Power Low-Noise CMOS Bio-Potential Amplifier for Neural Recording” IEEE Transactions on Circuits and Systems II, Express Briefs, Vol. 62, No. 10, pp. 927– 931, 2015.
• J. Lu, S. Young, I. Arel, J. Holleman, “A 1 TOPS/W Analog Deep Machine-Learning Engine with Floating-Gate Storage in 0.13 μm CMOS” IEEE Journal of Solid-State Circuits , Vol. 50, Issue 1, pp. 270–281, Jan. 2015.
• S. Young, J. Lu, J. Holleman, I. Arel, “On the Impact of Approximate Computation in an Analog DeSTIN Architecture,” IEEE Transactions on Neural Networks and Machine Learning, Vol.25, Issue 5, pp. 934–946, May 2014.

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