Intelligent Digital Systems Lab18
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
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Recent Submissions
RSS 1.0
RSS 2.0
ATOM 1.0
- Noise Tolerance of an Energy-Scalable Deep Learning Model with Two Extreme Bit-Precisions
- ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack
- Adaptive Input-to-Neuron Interlink Development in Training of Spike-Based Liquid State Machines
- DualPIM: A Dual-Precision and Low-Power CNN Inference Engine Using SRAM- and eDRAM-based Processing-in-Memory Arrays
- LightNorm: Area and Energy-Efficient Batch Normalization Hardware for On-Device DNN Training