Intelligent Digital Systems Lab22
Research topics
- High-performance system design for training deep neural networks
- Lightweight machine learning algorithms for energy-efficient inference engines
- Automated search of lightweight deep learning algorithms
- Energy-efficient hardware accelerator for scientific computing
- Understanding biological neurons by improving the performance of spike-based neural nets
- Brain signal processing using embedded hardware for the diagnosis of Parkinson’s disease
- Power-aware/thermal-aware system design methodology
Broader research interests include VLSI design, digital IC design, accelerator/computer architecture.
Please contact Prof. Kung if interested.
Advisor Professor : Kung, Jae Ha
Intelligent Digital Systems Lab Homepage
- High-performance system design for training deep neural networks
- Lightweight machine learning algorithms for energy-efficient inference engines
- Automated search of lightweight deep learning algorithms
- Energy-efficient hardware accelerator for scientific computing
- Understanding biological neurons by improving the performance of spike-based neural nets
- Brain signal processing using embedded hardware for the diagnosis of Parkinson’s disease
- Power-aware/thermal-aware system design methodology
Broader research interests include VLSI design, digital IC design, accelerator/computer architecture.
Please contact Prof. Kung if interested.
Advisor Professor : Kung, Jae Ha
Intelligent Digital Systems Lab Homepage
Co-Author(s)
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Recent Submissions
- Hardware accelerator for performing computation of deep neural network and electronic device including same
- 신경망 모델에 기반하여 고주파 생체 신호를 복원하는 방법 및 장치
- 완화된 프루닝을 통한 행렬 데이터 처리 방법 및 그 장치
- 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
- Noise Tolerance of an Energy-Scalable Deep Learning Model with Two Extreme Bit-Precisions