| UNPU: An energy-efficient deep neural network accelerator with fully variable weight bit precision J Lee, C Kim, S Kang, D Shin, S Kim, HJ Yoo IEEE Journal of Solid-State Circuits 54 (1), 173-185, 2018 | 370 | 2018 |
| UNPU: A 50.6 TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision J Lee, C Kim, S Kang, D Shin, S Kim, HJ Yoo 2018 IEEE International Solid-State Circuits Conference-(ISSCC), 218-220, 2018 | 309 | 2018 |
| 7.4 GANPU: A 135TFLOPS/W multi-DNN training processor for GANs with speculative dual-sparsity exploitation S Kang, D Han, J Lee, D Im, S Kim, S Kim, HJ Yoo 2020 IEEE International Solid-State Circuits Conference-(ISSCC), 140-142, 2020 | 67 | 2020 |
| A 13.7 TFLOPS/W floating-point DNN processor using heterogeneous computing architecture with exponent-computing-in-memory J Lee, J Kim, W Jo, S Kim, S Kim, J Lee, HJ Yoo 2021 Symposium on VLSI Circuits, 1-2, 2021 | 40 | 2021 |
| GANPU: An energy-efficient multi-DNN training processor for GANs with speculative dual-sparsity exploitation S Kang, D Han, J Lee, D Im, S Kim, S Kim, J Ryu, HJ Yoo IEEE Journal of Solid-State Circuits 56 (9), 2845-2857, 2021 | 36 | 2021 |
| 16.5 DynaPlasia: An eDRAM in-memory-computing-based reconfigurable spatial accelerator with triple-mode cell for dynamic resource switching S Kim, Z Li, S Um, W Jo, S Ha, J Lee, S Kim, D Han, HJ Yoo 2023 IEEE International Solid-State Circuits Conference (ISSCC), 256-258, 2023 | 31 | 2023 |
| A power-efficient CNN accelerator with similar feature skipping for face recognition in mobile devices S Kim, J Lee, S Kang, J Lee, HJ Yoo IEEE Transactions on Circuits and Systems I: Regular Papers 67 (4), 1181-1193, 2020 | 31 | 2020 |
| A 146.52 TOPS/W deep-neural-network learning processor with stochastic coarse-fine pruning and adaptive input/output/weight skipping S Kim, J Lee, S Kang, J Lee, HJ Yoo 2020 IEEE Symposium on VLSI Circuits, 1-2, 2020 | 30 | 2020 |
| C-DNN: A 24.5-85.8 TOPS/W complementary-deep-neural-network processor with heterogeneous CNN/SNN core architecture and forward-gradient-based sparsity generation S Kim, S Kim, S Hong, S Kim, D Han, HJ Yoo 2023 IEEE International Solid-State Circuits Conference (ISSCC), 334-336, 2023 | 25 | 2023 |
| Design of Sub-10-μW Sub-0.1% THD Sinusoidal Current Generator IC for Bio-Impedance Sensing K Kim, S Kim, HJ Yoo IEEE Journal of Solid-State Circuits 57 (2), 586-595, 2021 | 23 | 2021 |
| Neuro-CIM: A 310.4 TOPS/W neuromorphic computing-in-memory processor with low WL/BL activity and digital-analog mixed-mode neuron firing S Kim, S Kim, S Um, S Kim, K Kim, HJ Yoo 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and …, 2022 | 22 | 2022 |
| DynaPlasia: An eDRAM in-memory computing-based reconfigurable spatial accelerator with triple-mode cell S Kim, Z Li, S Um, W Jo, S Ha, J Lee, S Kim, D Han, HJ Yoo IEEE Journal of Solid-State Circuits 59 (1), 102-115, 2023 | 16 | 2023 |
| Neuro-CIM: ADC-less neuromorphic computing-in-memory processor with operation gating/stopping and digital–analog networks S Kim, S Kim, S Um, S Kim, K Kim, HJ Yoo IEEE Journal of Solid-State Circuits 58 (10), 2931-2945, 2023 | 16 | 2023 |
| PNPU: An energy-efficient deep-neural-network learning processor with stochastic coarse–fine level weight pruning and adaptive input/output/weight zero skipping S Kim, J Lee, S Kang, J Lee, W Jo, HJ Yoo IEEE Solid-State Circuits Letters 4, 22-25, 2020 | 15 | 2020 |
| TSUNAMI: Triple sparsity-aware ultra energy-efficient neural network training accelerator with multi-modal iterative pruning S Kim, J Lee, S Kang, D Han, W Jo, HJ Yoo IEEE Transactions on Circuits and Systems I: Regular Papers 69 (4), 1494-1506, 2022 | 13 | 2022 |
| An energy-efficient GAN accelerator with on-chip training for domain-specific optimization S Kim, S Kang, D Han, S Kim, S Kim, HJ Yoo IEEE Journal of Solid-State Circuits 56 (10), 2968-2980, 2021 | 13 | 2021 |
| 2.7 MetaVRain: A 133mW Real-Time Hyper-Realistic 3D-NeRF Processor with 1D-2D Hybrid-Neural Engines for Metaverse on Mobile Devices D Han, J Ryu, S Kim, S Kim, HJ Yoo 2023 IEEE International Solid-State Circuits Conference (ISSCC), 50-52, 2023 | 12 | 2023 |
| A low-power graph convolutional network processor with sparse grouping for 3d point cloud semantic segmentation in mobile devices S Kim, S Kim, J Lee, HJ Yoo IEEE Transactions on Circuits and Systems I: Regular Papers 69 (4), 1507-1518, 2022 | 12 | 2022 |
| ECIM: Exponent computing in memory for an energy-efficient heterogeneous floating-point DNN training processor J Lee, J Kim, W Jo, S Kim, S Kim, HJ Yoo IEEE Micro 42 (1), 99-107, 2021 | 12 | 2021 |
| C-dnn: An energy-efficient complementary deep-neural-network processor with heterogeneous cnn/snn core architecture S Kim, S Kim, S Hong, S Kim, D Han, J Choi, HJ Yoo IEEE Journal of Solid-State Circuits 59 (1), 157-172, 2023 | 11 | 2023 |