Anshul Jha

Anshul Jha

Anshul Jha

Assistant Professor
Stockton
Office:
Anderson 229
Email Address:
ajha@pacific.edu
Website:
https://sites.google.com/view/anshuljha/
Dr. Anshul Jha is an Assistant Professor at the University of the Pacific. Her research focuses on VLSI design, hardware accelerators, and energy-efficient computing systems for artificial intelligence and data-intensive applications. She works on specialized architectures and design methodologies that improve performance while reducing power consumption, memory requirements, and computational latency.
 
Her work bridges the gap between algorithm design and hardware implementation, with an emphasis on enabling real-time intelligence on resource-constrained platforms. By combining expertise in VLSI design, machine learning, and computer architecture, her research is directed towards advancing next-generation computing systems that are efficient, scalable, and deployable across a wide range of application domains, including healthcare, edge computing, and embedded systems.
Education
  • Ph.D., University of Texas at San Antonio, 2025
    Electrical Engineering
  • M.S., University of Texas at San Antonio, 2019
    Computer Engineering
Teaching Interests
  • Digital Design
  • VLSI Design
  • Hardware Accelerators for AI
Research Focus
  • VLSI Design
  • Low Power VLSI Design
  • Energy Efficient Hardware Accelerators
  • Hardware-software co-design for Neural Network systems
  • AI/ML

SELECTED PUBLICATIONS

  • A. Jha, S. Nag, I. D. S. Miranda, F. M. G. Franca, L. John, P. M. V. Lima and E. John, “Arrhythmia Classification at the Edge Using a Weightless Neural Network Hardware Accelerator”, The 22nd International Symposium on Applied Reconfigurable Computing (ARC-2026),  April 8-10, 2026, Cagliari, Italy.
  • Nag, S., Bacellar, A. T., Susskind, Z., Jha, A., Liberty, L., Sivakumar, A., ... & John, L. K. (2025, December). Ll-vit: Edge deployable vision transformers with look up table neurons. In 2025 International Conference on Field Programmable Technology (ICFPT) (pp. 19-29). IEEE.
  • Jha, G., Jha, A., & John, E. B. (2024, December). Performance Comparison of High Accuracy CNNs for Brain Tumor Detection Using Transfer Learning. In International Conference on Computational Science and Computational Intelligence (pp. 116-130). Cham: Springer Nature Switzerland.
  • Jha, A., John, E., & Banerjee, T. (2022, October). Multi-class classification of dementia from MRI images using transfer learning. In 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0597-0602). IEEE.
  • Jha, A., John, E., & Banerjee, T. (2022, August). Transfer Learning for COVID-19 and Pneumonia Detection using Chest X-Rays. In 2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 1-4). IEEE.