Thermal signature: a simple yet accurate thermal index for floorplan optimization. - Jaeha Kung; Inhak Han; Sachin S. Sapatnekar; Youngsoo Shin (DAC, 2011)

Compact thermal models: Assessment and pitfalls. - Jaeha Kung; Youngsoo Shin (ISOCC, 2011)

A power-aware digital feedforward neural network platform with backpropagation driven approximate synapses. - Jaeha Kung; Duckhwan Kim 001; Saibal Mukhopadhyay (ISLPED, 2015)

On the Impact of Energy-Accuracy Tradeoff in a Digital Cellular Neural Network for Image Processing. - Jaeha Kung; Duckhwan Kim 001; Saibal Mukhopadhyay (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2015)

ReRAM Crossbar based Recurrent Neural Network for human activity detection. - Yun Long; Eui Min Jung; Jaeha Kung; Saibal Mukhopadhyay (IJCNN, 2016)

Neurocube: A Programmable Digital Neuromorphic Architecture with High-Density 3D Memory. - Duckhwan Kim 001; Jaeha Kung; Sek M. Chai; Sudhakar Yalamanchili; Saibal Mukhopadhyay (ISCA, 2016)

Dynamic Approximation with Feedback Control for Energy-Efficient Recurrent Neural Network Hardware. - Jaeha Kung; Duckhwan Kim 001; Saibal Mukhopadhyay (ISLPED, 2016)

Adaptive weight compression for memory-efficient neural networks. - Jong Hwan Ko; Duckhwan Kim 001; Taesik Na; Jaeha Kung; Saibal Mukhopadhyay (DATE, 2017)

On-chip training of recurrent neural networks with limited numerical precision. - Taesik Na; Jong Hwan Ko; Jaeha Kung; Saibal Mukhopadhyay (IJCNN, 2017)

A Programmable Hardware Accelerator for Simulating Dynamical Systems. - Jaeha Kung; Yun Long; Duckhwan Kim 001; Saibal Mukhopadhyay (ISCA, 2017)

Energy-efficient neural image processing for Internet-of-Things edge devices. - Jong Hwan Ko; Yun Long; Mohammad Faisal Amir; Duckhwan Kim 001; Jaeha Kung; Taesik Na; Amit Ranjan Trivedi; Saibal Mukhopadhyay (MWSCAS, 2017)

A Power-Aware Digital Multilayer Perceptron Accelerator with On-Chip Training Based on Approximate Computing. - Duckhwan Kim 001; Jaeha Kung; Saibal Mukhopadhyay (IEEE Trans. Emerg. Top. Comput., 2017)

Energy-efficient digital hardware platform for learning complex systems. - Jaeha Kung (2017)

The CAMEL approach to stacked sensor smart cameras. - Saibal Mukhopadhyay; Marilyn Wolf; Mohammed Faisal Amir; Evan Gebhardt; Jong Hwan Ko; Jaeha Kung; Burhan Ahmad Musassar (DATE, 2018)

Maximizing system performance by balancing computation loads in LSTM accelerators. - Junki Park; Jaeha Kung; Wooseok Yi; Jae-Joon Kim (DATE, 2018)

Adaptive Precision Cellular Nonlinear Network. - Jaeha Kung; Duckhwan Kim 001; Saibal Mukhopadhyay (IEEE Trans. Very Large Scale Integr. Syst., 2018)

Efficient Object Detection Using Embedded Binarized Neural Networks. - Jaeha Kung; David C. Zhang; Gooitzen S. van der Wal; Sek M. Chai; Saibal Mukhopadhyay (J. Signal Process. Syst., 2018)

Peregrine: A Flexible Hardware Accelerator for LSTM with Limited Synaptic Connection Patterns. - Jaeha Kung; Junki Park; Sehun Park; Jae-Joon Kim (DAC, 2019)

Similarity-Based LSTM Architecture for Energy-Efficient Edge-Level Speech Recognition. - Junseo Jo; Jaeha Kung; Sunggu Lee; Youngjoo Lee (ISLPED, 2019)

WMixNet: An Energy-Scalable and Computationally Lightweight Deep Learning Accelerator. - Sangwoo Jung; Seungsik Moon; Youngjoo Lee; Jaeha Kung (ISLPED, 2019)

Noise Tolerance of an Energy-Scalable Deep Learning Model with Two Extreme Bit-Precisions. - Sangwoo Jung; Jaeha Kung (ISOCC, 2019)

Defending Against Flush+Reload Attack With DRAM Cache by Bypassing Shared SRAM Cache. - Minwoo Jang; Seungkyu Lee 002; Jaeha Kung; Daehoon Kim (IEEE Access, 2020)

Towards Scalable Analytics with Inference-Enabled Solid-State Drives. - Minsub Kim; Jaeha Kung; Sungjin Lee 001 (IEEE Comput. Archit. Lett., 2020)

Balancing Computation Loads and Optimizing Input Vector Loading in LSTM Accelerators. - Junki Park; Wooseok Yi; Daehyun Ahn; Jaeha Kung; Jae-Joon Kim (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2020)

ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack. - Dahoon Park; Kon-Woo Kwon; Sunghoon Im; Jaeha Kung (BMVC, 2021)

Adaptive Input-to-Neuron Interlink Development in Training of Spike-Based Liquid State Machines. - Sangwoo Hwang; Junghyup Lee; Jaeha Kung (ISCAS, 2021)

Deep Partitioned Training From Near-Storage Computing to DNN Accelerators. - Yongjoo Jang; Sejin Kim; Daehoon Kim; Sungjin Lee 001; Jaeha Kung (IEEE Comput. Archit. Lett., 2021)

Design and Analysis of Approximate Compressors for Balanced Error Accumulation in MAC Operator. - Gunho Park; Jaeha Kung; Youngjoo Lee (IEEE Trans. Circuits Syst. I Regul. Pap., 2021)

High-throughput Near-Memory Processing on CNNs with 3D HBM-like Memory. - Naebeom Park; Sungju Ryu; Jaeha Kung; Jae-Joon Kim (ACM Trans. Design Autom. Electr. Syst., 2021)

DualPIM: A Dual-Precision and Low-Power CNN Inference Engine Using SRAM- and eDRAM-based Processing-in-Memory Arrays. - Sangwoo Jung; Jaehyun Lee; Huiseong Noh; Jong-Hyeok Yoon; Jaeha Kung (AICAS, 2022)

LightNorm: Area and Energy-Efficient Batch Normalization Hardware for On-Device DNN Training. - Seock-Hwan Noh; Junsang Park; Dahoon Park; Jahyun Koo 002; Jeik Choi; Jaeha Kung (ICCD, 2022)

SEMS: Scalable Embedding Memory System for Accelerating Embedding-Based DNNs. - Sejin Kim; Jungwoo Kim; Yongjoo Jang; Jaeha Kung; Sungjin Lee 001 (IEEE Comput. Archit. Lett., 2022)

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. - Arup K. George; Wooyoon Shim; Jaeha Kung; Ji-Hoon Kim 003; Minkyu Je; Junghyup Lee (IEEE Trans. Circuits Syst. I Regul. Pap., 2022)

AutoRelax: HW-SW Co-Optimization for Efficient SpGEMM Operations With Automated Relaxation in Deep Learning. - Sehun Park; Jae-Joon Kim; Jaeha Kung (IEEE Trans. Emerg. Top. Comput., 2022)

Implication of Optimizing NPU Dataflows on Neural Architecture Search for Mobile Devices. - Jooyeon Lee; Junsang Park; Seunghyun Lee; Jaeha Kung (ACM Trans. Design Autom. Electr. Syst., 2022)

DBPS: Dynamic Block Size and Precision Scaling for Efficient DNN Training Supported by RISC-V ISA Extensions. - Seunghyun Lee; Jeik Choi; Seock-Hwan Noh; Jahyun Koo 002; Jaeha Kung (DAC, 2023)

A 1V 136.6dB-DR 4kHz-BW $ Delta Sigma$ Current-to-Digital Converter with a Truncation-Noise-Shaped Baseline-Servo-Loop in 0.18 mu mathrm{m}$ CMOS. - Taeryoung Seol; Sehwan Lee; Geunha Kim; Samhwan Kim; Euiseong Kim; Seungyeob Baik; Jaeha Kung; Ji-Woong Choi; Arup K. George; Junghyup Lee (ISSCC, 2023)

Improving Hardware Efficiency of a Sparse Training Accelerator by Restructuring a Reduction Network. - Banseok Shin; Sehun Park; Jaeha Kung (NEWCAS, 2023)

All-rounder: A flexible DNN accelerator with diverse data format support. - Seock-Hwan Noh; Seungpyo Lee; Banseok Shin; Sehun Park; Yongjoo Jang; Jaeha Kung (CoRR, 2023)

Simplified Compressor and Encoder Designs for Low-Cost Approximate Radix-4 Booth Multiplier. - Gunho Park; Jaeha Kung; Youngjoo Lee (IEEE Trans. Circuits Syst. II Express Briefs, 2023)

FlexBlock: A Flexible DNN Training Accelerator With Multi-Mode Block Floating Point Support. - Seock-Hwan Noh; Jahyun Koo 002; Seunghyun Lee; Jongse Park; Jaeha Kung (IEEE Trans. Computers, 2023)

NDPipe: Exploiting Near-data Processing for Scalable Inference and Continuous Training in Photo Storage. - Jungwoo Kim; Seonggyun Oh; Jaeha Kung; Yeseong Kim; Sungjin Lee 001 (ASPLOS (3), 2024)

A 65nm 687.5-TOPS/W Drive Strength-based SRAM Compute-In-Memory Macro with Adaptive Dynamic Range for Edge AI applications. - Dong-gu Choi; Jaehyun Lee; Jahyun Koo 002; Woo Kyoung Han; Dahoon Park; Jaeha Kung; Junghyup Lee; Jong-Hyeok Yoon (A-SSCC, 2024)

A 3.3-to-11V-Supply-Range 10μW/Ch Arbitrary-Waveform-Capable Neural Stimulator with Output-Adaptive-Self-Bias and Supply-Tracking Schemes in 0.18μm Standard CMOS. - Jeongyoon Wie; Sangwoo Jung; Taeryoung Seol; Geunha Kim; Sehwan Lee; Homin Jang; Samhwan Kim; Yeonjae Shin; Jae Eun Jang; Jaeha Kung; Arup K. George; Junghyup Lee (CICC, 2024)

OPAL: Outlier-Preserved Microscaling Quantization Accelerator for Generative Large Language Models. - Jahyun Koo 002; Dahoon Park; Sangwoo Jung; Jaeha Kung (DAC, 2024)

A Full SW-HW Demonstration of GEMM Accelerators with RISC-V Instruction Extensions. - Seonghun Jeong; Jooyeon Lee; Jaeha Kung (ICEIC, 2024)

A Refresh-Free Near-eDRAM AES Accelerator for Secure Underwater Communications. - Hyunseob Shin; Yejin Lee 016; Jaeha Kung (ICTC, 2024)

A Ready-to-Use RTL Generator for Systolic Tensor Arrays and Analysis Using Open-Source EDA Tools. - Jooyeon Lee; Donghun Lee; Jaeha Kung (ISCAS, 2024)

SpikedAttention: Training-Free and Fully Spike-Driven Transformer-to-SNN Conversion with Winner-Oriented Spike Shift for Softmax Operation. - Sangwoo Hwang; Seunghyun Lee; Dahoon Park; Donghun Lee; Jaeha Kung (NeurIPS, 2024)

Skipformer: Evolving Beyond Blocks for Extensively Searching On-Device Language Models With Learnable Attention Window. - Matthew Bodenham; Jaeha Kung (IEEE Access, 2024)

A Dual-Precision and Low-Power CNN Inference Engine Using a Heterogeneous Processing-in-Memory Architecture. - Sangwoo Jung; Jaehyun Lee; Dahoon Park; Youngjoo Lee; Jong-Hyeok Yoon; Jaeha Kung (IEEE Trans. Circuits Syst. I Regul. Pap., 2024)

RISC-V Driven Orchestration of Vector Processing Units and eFlash Compute-in-Memory Arrays for Fast and Accurate Keyword Spotting. - Gunil Kang; Dahoon Park; Hojin Lee; Sangwoo Jung; Jiyong Park; Jung Gyu Min; Youngjoo Lee; Jaeha Kung (ASP-DAC, 2025)

FlexENM: A Flexible Encrypting-Near-Memory with Refresh-Less eDRAM-Based Multi-Mode AES. - Hyunseob Shin; Jaeha Kung (DATE, 2025)

FlexNeRFer: A Multi-Dataflow, Adaptive Sparsity-Aware Accelerator for On-Device NeRF Rendering. - Seock-Hwan Noh; Banseok Shin; Jeik Choi; Seungpyo Lee; Jaeha Kung; Yeseong Kim (ISCA, 2025)

One-Spike SNN: Single-Spike Phase Coding With Base Manipulation for ANN-to-SNN Conversion Loss Minimization. - Sangwoo Hwang; Jaeha Kung (IEEE Trans. Emerg. Top. Comput., 2025)

All-Rounder: A Flexible AI Accelerator With Diverse Data Format Support and Morphable Structure for Multi-DNN Processing. - Seock-Hwan Noh; Seungpyo Lee; Banseok Shin; Sehun Park; Yongjoo Jang; Jaeha Kung (IEEE Trans. Very Large Scale Integr. Syst., 2025)

Jack Unit: An Area- and Energy-Efficient Multiply-Accumulate (MAC) Unit Supporting Diverse Data Formats. - Seock-Hwan Noh; Sungju Kim; Seohyun Kim; Daehoon Kim; Jaeha Kung; Yeseong Kim (CoRR, 2025)

FlashVault: Versatile In-NAND Self-Encryption with Zero Area Overhead. - Seock-Hwan Noh; Hoyeon Lee; Junkyum Kim; Junsu Im; Jay H. Park; Sungjin Lee 001; Sam H. Noh; Yeseong Kim; Jaeha Kung (CoRR, 2025)