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ORCIDJaeha Kung
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2020 – today
- 2024
[j17]Matthew Bodenham
, Jaeha Kung:
Skipformer: Evolving Beyond Blocks for Extensively Searching On-Device Language Models With Learnable Attention Window. IEEE Access 12: 124428-124439 (2024)- [c27]Jungwoo Kim, Seonggyun Oh, Jaeha Kung, Yeseong Kim, Sungjin Lee:
NDPipe: Exploiting Near-data Processing for Scalable Inference and Continuous Training in Photo Storage. ASPLOS (3) 2024: 689-707 - [c26]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:
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. CICC 2024: 1-2 - [c25]Seonghun Jeong, Jooyeon Lee, Jaeha Kung:
A Full SW-HW Demonstration of GEMM Accelerators with RISC-V Instruction Extensions. ICEIC 2024: 1-3 - [c24]Jooyeon Lee, Donghun Lee, Jaeha Kung:
A Ready-to-Use RTL Generator for Systolic Tensor Arrays and Analysis Using Open-Source EDA Tools. ISCAS 2024: 1-5 - [i6]Sangwoo Hwang, Jaeha Kung:
One-Spike SNN: Single-Spike Phase Coding with Base Manipulation for ANN-to-SNN Conversion Loss Minimization. CoRR abs/2403.08786 (2024) - [i5]Jahyun Koo, Dahoon Park, Sangwoo Jung, Jaeha Kung:
OPAL: Outlier-Preserved Microscaling Quantization Accelerator for Generative Large Language Models. CoRR abs/2409.05902 (2024) - 2023
- [j16]Seock-Hwan Noh, Jahyun Koo, Seunghyun Lee, Jongse Park, Jaeha Kung:
FlexBlock: A Flexible DNN Training Accelerator With Multi-Mode Block Floating Point Support. IEEE Trans. Computers 72(9): 2522-2535 (2023) - [j15]Gunho Park, Jaeha Kung, Youngjoo Lee:
Simplified Compressor and Encoder Designs for Low-Cost Approximate Radix-4 Booth Multiplier. IEEE Trans. Circuits Syst. II Express Briefs 70(3): 1154-1158 (2023) - [c23]Seunghyun Lee, Jeik Choi, Seock-Hwan Noh, Jahyun Koo, Jaeha Kung:
DBPS: Dynamic Block Size and Precision Scaling for Efficient DNN Training Supported by RISC-V ISA Extensions. DAC 2023: 1-6 - [c22]Taeryoung Seol, Sehwan Lee, Geunha Kim, Samhwan Kim, Euiseong Kim, Seungyeob Baik, Jaeha Kung, Ji-Woong Choi, Arup K. George, Junghyup Lee:
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. ISSCC 2023: 482-483 - [c21]Banseok Shin, Sehun Park, Jaeha Kung:
Improving Hardware Efficiency of a Sparse Training Accelerator by Restructuring a Reduction Network. NEWCAS 2023: 1-5 - [i4]Seock-Hwan Noh, Seungpyo Lee, Banseok Shin, Sehun Park, Yongjoo Jang, Jaeha Kung:
All-rounder: A flexible DNN accelerator with diverse data format support. CoRR abs/2310.16757 (2023) - 2022
- [j14]Sejin Kim, Jungwoo Kim, Yongjoo Jang, Jaeha Kung, Sungjin Lee:
SEMS: Scalable Embedding Memory System for Accelerating Embedding-Based DNNs. IEEE Comput. Archit. Lett. 21(2): 157-160 (2022) - [j13]Arup K. George, Wooyoon Shim, Jaeha Kung, Ji-Hoon Kim, Minkyu Je, Junghyup 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 Trans. Circuits Syst. I Regul. Pap. 69(3): 1171-1184 (2022) - [j12]Sehun Park, Jae-Joon Kim, Jaeha Kung:
AutoRelax: HW-SW Co-Optimization for Efficient SpGEMM Operations With Automated Relaxation in Deep Learning. IEEE Trans. Emerg. Top. Comput. 10(3): 1428-1442 (2022) - [j11]Jooyeon Lee, Junsang Park, Seunghyun Lee, Jaeha Kung:
Implication of Optimizing NPU Dataflows on Neural Architecture Search for Mobile Devices. ACM Trans. Design Autom. Electr. Syst. 27(5): 48:1-48:24 (2022) - [c20]Sangwoo Jung, Jaehyun Lee, Huiseong Noh, Jong-Hyeok Yoon, Jaeha Kung:
DualPIM: A Dual-Precision and Low-Power CNN Inference Engine Using SRAM- and eDRAM-based Processing-in-Memory Arrays. AICAS 2022: 70-73 - [c19]Seock-Hwan Noh, Junsang Park, Dahoon Park, Jahyun Koo, Jeik Choi, Jaeha Kung:
LightNorm: Area and Energy-Efficient Batch Normalization Hardware for On-Device DNN Training. ICCD 2022: 443-450 - [i3]Seock-Hwan Noh, Jahyun Koo, Seunghyun Lee, Jongse Park, Jaeha Kung:
FlexBlock: A Flexible DNN Training Accelerator with Multi-Mode Block Floating Point Support. CoRR abs/2203.06673 (2022) - [i2]Seock-Hwan Noh, Junsang Park, Dahoon Park, Jahyun Koo, Jeik Choi, Jaeha Kung:
LightNorm: Area and Energy-Efficient Batch Normalization Hardware for On-Device DNN Training. CoRR abs/2211.02686 (2022) - 2021
- [j10]Yongjoo Jang, Sejin Kim, Daehoon Kim, Sungjin Lee, Jaeha Kung:
Deep Partitioned Training From Near-Storage Computing to DNN Accelerators. IEEE Comput. Archit. Lett. 20(1): 70-73 (2021) - [j9]Gunho Park, Jaeha Kung, Youngjoo Lee:
Design and Analysis of Approximate Compressors for Balanced Error Accumulation in MAC Operator. IEEE Trans. Circuits Syst. I Regul. Pap. 68(7): 2950-2961 (2021) - [j8]Naebeom Park, Sungju Ryu, Jaeha Kung, Jae-Joon Kim:
High-throughput Near-Memory Processing on CNNs with 3D HBM-like Memory. ACM Trans. Design Autom. Electr. Syst. 26(6): 48:1-48:20 (2021) - [c18]Dahoon Park, Kon-Woo Kwon, Sunghoon Im, Jaeha Kung:
ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack. BMVC 2021: 48 - [c17]Sangwoo Hwang, Junghyup Lee, Jaeha Kung:
Adaptive Input-to-Neuron Interlink Development in Training of Spike-Based Liquid State Machines. ISCAS 2021: 1-5 - [i1]Dahoon Park, Kon-Woo Kwon, Sunghoon Im, Jaeha Kung:
ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack. CoRR abs/2111.01080 (2021) - 2020
- [j7]Minwoo Jang, Seungkyu Lee, Jaeha Kung, Daehoon Kim:
Defending Against Flush+Reload Attack With DRAM Cache by Bypassing Shared SRAM Cache. IEEE Access 8: 179837-179844 (2020) - [j6]Minsub Kim, Jaeha Kung, Sungjin Lee:
Towards Scalable Analytics with Inference-Enabled Solid-State Drives. IEEE Comput. Archit. Lett. 19(1): 13-17 (2020) - [j5]Junki Park, Wooseok Yi, Daehyun Ahn, Jaeha Kung, Jae-Joon Kim:
Balancing Computation Loads and Optimizing Input Vector Loading in LSTM Accelerators. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39(9): 1889-1901 (2020)
2010 – 2019
- 2019
- [c16]Jaeha Kung, Junki Park, Sehun Park, Jae-Joon Kim:
Peregrine: A Flexible Hardware Accelerator for LSTM with Limited Synaptic Connection Patterns. DAC 2019: 209 - [c15]Junseo Jo, Jaeha Kung, Sunggu Lee, Youngjoo Lee:
Similarity-Based LSTM Architecture for Energy-Efficient Edge-Level Speech Recognition. ISLPED 2019: 1-6 - [c14]Sangwoo Jung, Seungsik Moon, Youngjoo Lee, Jaeha Kung:
WMixNet: An Energy-Scalable and Computationally Lightweight Deep Learning Accelerator. ISLPED 2019: 1-6 - [c13]Sangwoo Jung, Jaeha Kung:
Noise Tolerance of an Energy-Scalable Deep Learning Model with Two Extreme Bit-Precisions. ISOCC 2019: 71-72 - 2018
- [j4]Jaeha Kung, Duckhwan Kim, Saibal Mukhopadhyay:
Adaptive Precision Cellular Nonlinear Network. IEEE Trans. Very Large Scale Integr. Syst. 26(5): 841-854 (2018) - [j3]Jaeha Kung, David C. Zhang, Gooitzen S. van der Wal, Sek M. Chai, Saibal Mukhopadhyay:
Efficient Object Detection Using Embedded Binarized Neural Networks. J. Signal Process. Syst. 90(6): 877-890 (2018) - [c12]Junki Park, Jaeha Kung, Wooseok Yi, Jae-Joon Kim:
Maximizing system performance by balancing computation loads in LSTM accelerators. DATE 2018: 7-12 - [c11]Saibal Mukhopadhyay, Marilyn Wolf, Mohammed Faisal Amir, Evan Gebhardt, Jong Hwan Ko, Jaeha Kung, Burhan Ahmad Musassar:
The CAMEL approach to stacked sensor smart cameras. DATE 2018: 1299-1303 - 2017
- [b1]Jaeha Kung:
Energy-efficient digital hardware platform for learning complex systems. Georgia Institute of Technology, Atlanta, GA, USA, 2017 - [j2]Duckhwan Kim, Jaeha Kung, Saibal Mukhopadhyay:
A Power-Aware Digital Multilayer Perceptron Accelerator with On-Chip Training Based on Approximate Computing. IEEE Trans. Emerg. Top. Comput. 5(2): 164-178 (2017) - [c10]Jong Hwan Ko, Duckhwan Kim, Taesik Na, Jaeha Kung, Saibal Mukhopadhyay:
Adaptive weight compression for memory-efficient neural networks. DATE 2017: 199-204 - [c9]Taesik Na, Jong Hwan Ko, Jaeha Kung, Saibal Mukhopadhyay:
On-chip training of recurrent neural networks with limited numerical precision. IJCNN 2017: 3716-3723 - [c8]Jaeha Kung, Yun Long, Duckhwan Kim, Saibal Mukhopadhyay:
A Programmable Hardware Accelerator for Simulating Dynamical Systems. ISCA 2017: 403-415 - [c7]Jong Hwan Ko, Yun Long, Mohammad Faisal Amir, Duckhwan Kim, Jaeha Kung, Taesik Na, Amit Ranjan Trivedi, Saibal Mukhopadhyay:
Energy-efficient neural image processing for Internet-of-Things edge devices. MWSCAS 2017: 1069-1072 - 2016
- [c6]Yun Long, Eui Min Jung, Jaeha Kung, Saibal Mukhopadhyay:
ReRAM Crossbar based Recurrent Neural Network for human activity detection. IJCNN 2016: 939-946 - [c5]Duckhwan Kim, Jaeha Kung, Sek M. Chai, Sudhakar Yalamanchili, Saibal Mukhopadhyay:
Neurocube: A Programmable Digital Neuromorphic Architecture with High-Density 3D Memory. ISCA 2016: 380-392 - [c4]Jaeha Kung, Duckhwan Kim, Saibal Mukhopadhyay:
Dynamic Approximation with Feedback Control for Energy-Efficient Recurrent Neural Network Hardware. ISLPED 2016: 168-173 - 2015
- [j1]Jaeha Kung, Duckhwan Kim, Saibal Mukhopadhyay:
On the Impact of Energy-Accuracy Tradeoff in a Digital Cellular Neural Network for Image Processing. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 34(7): 1070-1081 (2015) - [c3]Jaeha Kung, Duckhwan Kim, Saibal Mukhopadhyay:
A power-aware digital feedforward neural network platform with backpropagation driven approximate synapses. ISLPED 2015: 85-90 - 2011
- [c2]Jaeha Kung, Inhak Han, Sachin S. Sapatnekar, Youngsoo Shin:
Thermal signature: a simple yet accurate thermal index for floorplan optimization. DAC 2011: 108-113 - [c1]Jaeha Kung, Youngsoo Shin:
Compact thermal models: Assessment and pitfalls. ISOCC 2011: 337-340
Coauthor Index
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last updated on 2024-10-10 22:19 CEST by the dblp team
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