7.6 A 65nm 236.5 nJ/classification neuromorphic processor with 7.5% energy overhead on-chip learning using direct spike-only feedback J Park, J Lee, D Jeon 2019 IEEE International Solid-State Circuits Conference-(ISSCC), 140-142, 2019 | 80 | 2019 |
A 65-nm neuromorphic image classification processor with energy-efficient training through direct spike-only feedback J Park, J Lee, D Jeon IEEE Journal of Solid-State Circuits 55 (1), 108-119, 2019 | 49 | 2019 |
9.3 a 40nm 4.81 TFLOPS/W 8b floating-point training processor for non-sparse neural networks using shared exponent bias and 24-way fused multiply-add tree J Park, S Lee, D Jeon 2021 IEEE International Solid-State Circuits Conference (ISSCC) 64, 1-3, 2021 | 40 | 2021 |
A neural network training processor with 8-bit shared exponent bias floating point and multiple-way fused multiply-add trees J Park, S Lee, D Jeon IEEE Journal of Solid-State Circuits 57 (3), 965-977, 2021 | 24 | 2021 |
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization S Lee, J Park, D Jeon International Conference on Learning Representations, 2021 | 9 | 2021 |
Activation Sharing with Asymmetric Paths Solves Weight Transport Problem without Bidirectional Connection S Woo, J Park, J Hong, D Jeon Advances in Neural Information Processing Systems 34, 29697-29709, 2021 | 2 | 2021 |