Deep In-Memory Architectures for Machine Learning-Accuracy Versus Efficiency Trade-Offs
- Mingu Kang
- Yongjune Kim
- et al.
- 2020
- IEEE TCAS-I
Paper
Publications
10 results for Naresh R. Shanbhag
An Energy-Efficient Programmable Mixed-Signal Accelerator for Machine Learning Algorithms
- Mingu Kang
- Prakalp Srivastava
- et al.
- 2019
- IEEE Micro
PaperAn MRAM-based deep in-memory architecture for deep neural networks
- Ameya D. Patil
- Haocheng Hua
- et al.
- 2019
- ISCAS 2019
Conference paperAccumulation bit-width scaling for ultra-low precision training of deep networks
- Charbel Sakr
- Naigang Wang
- et al.
- 2019
- ICLR 2019
Conference paperA variation-tolerant in-memory machine learning classifier via on-chip training
- Sujan Gonugondla
- Mingu Kang
- et al.
- 2018
- IEEE JSSC
PaperGeneralized water-filling for source-aware energy-efficient SRAMs
- Yongjune Kim
- Mingu Kang
- et al.
- 2018
- IEEE Transactions on Communications
PaperTrue Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization
- Charbel Sakr
- Jungwook Choi
- et al.
- 2018
- ICASSP 2018
Conference paperAn In-Memory VLSI Architecture for Convolutional Neural Networks
- Mingu Kang
- Sungmin Lim
- et al.
- 2018
- IEEE JESTCS
PaperPROMISE: An end-to-End design of a programmable mixed-Signal accelerator for Machine-Learning algorithms
- Prakalp Srivastava
- Mingu Kang
- et al.
- 2018
- ISCA 2018
Conference paperAnalysis of error resiliency of belief propagation in computer vision
- Jungwook Choi
- Ameya D. Patii
- et al.
- 2016
- ICASSP 2016
Conference paper