Gope, Dibakar

Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers

Banbury, C., Zhou, C., Fedorov, I., Navarro, R. M., Thakker, U., Gope, D., Reddi, V. J., Mattina, M., & Whatmough, P. N. (2020) · arXiv

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 183

Compressing RNNs for IoT devices by 15-38x using Kronecker Products

Thakker, U., Beu, J., Gope, D., Zhou, C., Fedorov, I., Dasika, G., & Mattina, M. (2019) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 30

Jumping through Local Minima: Quantization in the Loss Landscape of Vision Transformers

Frumkin, N., Gope, D., & Marculescu, D. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsStochastic Gradient Optimization Techniques

Cited by 14

Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products

Thakker, U., Fedorov, I., Zhou, C., Gope, D., Mattina, M., Dasika, G., & Beu, J. (2021) · ACM Journal on Emerging Technologies in Computing Systems

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 8

CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers

Frumkin, N., Gope, D., & Marculescu, D. (2022) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 1