Journals

2025

1. S.-H. Noh, S. Lee, B. Shin, S. Park, Y. Jang, J. Kung, "All-rounder: A flexible AI accelerator with diverse data format support and morphable structure for multi-DNN processing," under revision at IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

2024

1. S. Jung, J. Lee, D. Park, Y. Lee, J.-H. Yoon, J. Kung, "A dual-precision and low-power CNN inference engine using a heterogeneous processing-in-memory architecture," IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), May 2024.

2. S. Hwang and J. Kung, "One-Spike SNN: single-spike phase coding with base manipulation for ANN-to-SNN conversion loss minimization," IEEE Transactions on Emerging Topics in Computing (TETC), April 2024.

3. M. Bodenham and J. Kung, "Skipformer: Evolving Beyond Blocks for Extensively Searching On-Device Language Models with Learnable Attention Window," IEEE Access, September 2024.

2023

1. S.-H. Noh*, J. Koo*, S. Lee, J. Park, J. Kung, “FlexBlock: A flexible DNN training accelerator with multi-mode block floating point support,” IEEE Transactions on Computers (TC), March 2023. *Equally Contributed Authors

2. S. Hwang, Y. Hwang, D. Kim, J. Lee, H. K. Choi, J. Lee, H. Kang, J. Kung, “ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network,” Frontiers in Neuroscience, August 2023.

2022

1. J. Lee*, J. Park*, S. Lee, J. Kung, “Implication of optimizing NPU dataflows on neural architecture search for mobile devices,” ACM Transactions on Design Automation of Electronic Systems (TODAES), January 2022. *Equally Contributed Authors

2. G. Park, J. Kung, Y. Lee, “Simplified compressor and encoder designs for low-cost approximate radix-4 booth multiplier,” IEEE Transactions on Circuits and Systems II: Express Briefs (TCAS-II), October 2022.

3. S. Kim, J. Kim, Y. Jang, J. Kung, S. Lee, “SEMS: Scalable embedding memory system exploiting near-data processing,” IEEE Computer Architecture Letters (CAL), Nov. 2022.

2021

1. A. K. George, W. Shim, J. Kung, J.-H. Kim, M. Je, J. Lee, "A 46-nF/10-MΩ Range 114-aF/0.37-Ω Resolution Parasitic-and Temperature-Insensitive Reconfigurable RC-to-Digital Converter in 0.18-μm CMOS," IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), December 2021.

2. S. Park, J.-J. Kim, J. Kung, “AutoRelax: HW-SW co-optimization for efficient spGEMM operations with automated relaxation in deep learning,” IEEE Transactions on Emerging Topics in Computing (TETC), June 2021.

3. N. Park, S. Ryu, J. Kung, J.-J. Kim, “High-throughput near-memory processing on CNNs with 3D HBM-like memory,” ACM Transactions on Design Automation of Electronic Systems (TODAES), June 2021.

4. Y. Jang, S. Kim, D. Kim, S. Lee, J. Kung, “Deep partitioned training from near-storage computing to DNN accelerators,” IEEE Computer Architecture Letters (CAL), May 2021 (lightning talk: link).

5. G. Park, J. Kung, Y. Lee, “Design and analysis of approximate compressors for balanced error accumulation in MAC operator,” IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), April 2021.

2020

1. J. Jo, J. Kung, and Y. Lee, “Approximate LSTM computing for energy-efficient speech recognition,” MDPI Electronics, November 2020.

2. M. Jang, S. Lee, J. Kung, D. Kim, “Defending against flush+reload attack with DRAM cache by bypassing shared SRAM cache,” IEEE Access, September 2020.

2019

1. M. Kim, J. Kung, S. Lee, “Towards scalable analytics with inference-enabled solid-state drives,” IEEE Computer Architecture Letters (CAL), July 2019.

2. J. Park, W. Yi, D. Ahn, J. Kung, J.-J. Kim, “Balancing computation loads and optimizing input vector loading in LSTM accelerators,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), July 2019.

2015-2018

1. J. Kung, D. Kim, S. Mukhopadhyay, “Adaptive precision cellular nonlinear network,” IEEE Transcations on Very Large Scale Integration Systems (TVLSI), Feb. 2018.

2. J. Kung, D. Zhang, G. van der Wal, S. Chai, S. Mukhopadhyay, “Efficient object detection using embedded binarized neural networks,” Journal of Signal Processing Systems (JSPS): Special Issue on Embedded Computer Vision, June 2017.

3. D. Kim, J. Kung, S. Mukhopadhyay, “A power-aware digital multilayer perceptron accelerator with on-chip training based on approximate computing,” IEEE Transactions on Emerging Topics in Computing (TETC), May 2017.

4. J. Kung, D. Kim, S. Mukhopadhyay, “On the impact of energy-accuracy tradeoff in a digital cellular neural network for image processing,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), July 2015.

5. J. Kung, W. Yueh, S. Yalamanchili, S. Mukhopadhyay, “Post-silicon estimation of spatiotemporal temperature variations using MIMO thermal filters,” IEEE Transactions on Components, Packaging and Manufacturing Technology (TCPMT), May 2015.