| 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 | 61 | 2020 |
| 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 | 29 | 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 | 15 | 2022 |
| An overview of sparsity exploitation in CNNs for on-device intelligence with software-hardware cross-layer optimizations S Kang, G Park, S Kim, S Kim, D Han, HJ Yoo IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11 (4 …, 2021 | 13 | 2021 |
| A 0.5 V 9.26 μW 15.28 mΩ/√ Hz Bio-Impedance Sensor IC With 0.55° Overall Phase Error. K Kim, JH Kim, S Gweon, J Lee, M Kim, Y Lee, S Kim, HJ Yoo ISSCC, 364-366, 2019 | 12 | 2019 |
| 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 | 11 | 2023 |
| A 64.1 mW accurate real-time visual object tracking processor with spatial early stopping on siamese network S Kim, S Kim, S Kim, D Han, HJ Yoo IEEE Transactions on Circuits and Systems II: Express Briefs 68 (5), 1675-1679, 2021 | 11 | 2021 |
| An Energy-Efficient GAN Accelerator With On-Chip Training for Domain-Specific Optimization SKHY S Kim, S Kang, D Han, S Kim IEEE Journal of Solid-State Circuits 56 (10), 2968 - 2980, 2021 | 9 | 2021 |
| SNPU: Always-on 63.2 μW Face Recognition Spike Domain Convolutional Neural Network Processor with Spike Train Decomposition and Shift-and-Accumulation Unit S Kim, S Kim, S Um, S Kim, J Lee, HJ Yoo 2022 IEEE Asian Solid-State Circuits Conference (A-SSCC), 2-4, 2022 | 5 | 2022 |
| 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, 2023 | 3 | 2023 |
| A 49.5 mW multi-scale linear quantized online learning processor for real-time adaptive object detection S Song, S Kim, G Park, D Han, HJ Yoo IEEE Transactions on Circuits and Systems II: Express Briefs 69 (5), 2443-2447, 2022 | 3 | 2022 |
| A Reconfigurable 1T1C eDRAM-based Spiking Neural Network Computing-In-Memory Processor for High System-Level Efficiency S Kim, S Kim, S Um, S Kim, Z Li, S Kim, W Jo, H Yoo 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2023 | 2 | 2023 |
| SNPU: An energy-efficient spike domain deep-neural-network processor with two-step spike encoding and shift-and-accumulation unit S Kim, S Kim, S Um, S Kim, J Lee, HJ Yoo IEEE Journal of Solid-State Circuits, 2023 | 2 | 2023 |
| COOL-NPU: Complementary online learning neural processing unit with CNN-SNN heterogeneous core and event-driven backpropagation S Kim, S Kim, S Hong, S Kim, D Han, J Choi, HJ Yoo 2023 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS), 1-3, 2023 | 2 | 2023 |
| An Energy-Efficient GAN Accelerator with On-chip Training for Domain Specific Optimization S Kim, S Kang, D Han, S Kim, S Kim, H Yoo 2020 IEEE Asian Solid-State Circuits Conference (A-SSCC), 1-4, 2020 | | 2020 |
| GANPU: A Versatile Many-Core Processor for Training GAN on Mobile Devices with Speculative Dual-Sparsity Exploitation S Kang, HJ Yoo, D Han, J LEE, DS Im, S Kim, S Kim, J RYU Hot Chips: A Symposium on High Performance Chips, 2020 | | 2020 |
