Taesik Na

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• [J8] CAMEL: An Adaptive Camera with Embedded Machine Learning Based Sensor Parameter Control
• [C24] A Camera with Brain–Embedding Machine Learning in 3D Sensors
• [C23] Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
• [C22] Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms
• [J6] 3-D Stacked Image Sensor With Deep Neural Network Computation
• [J5] An Energy-Quality Scalable Wireless Image Sensor Node for Object-Based Video Surveillance
• [C19] Cascade Adversarial Machine Learning Regularized with a Unified Embedding
• [C16] A Unified Embedding for Image Classification and Pixel-Level Regularization
• [J4] Design and Analysis of a Neural Network Inference Engine based on Adaptive Weight Compression
• [C13] Cascade Adversarial Machine Learning Regularized with a Unified Embedding
• [C12] Energy-Efficient Neural Image Processing for Internet-of-Things Edge Devices
• [C11] Design of an Energy-Efficient Accelerator for Training of Convolutional Neural Networks using Frequency-Domain Computation
• [J3] A Single-Chip Image Sensor Node With Energy Harvesting From a CMOS Pixel Array
• [J2] Clock Data Compensation Aware Digital Circuits Design for Voltage Margin Reduction
• [C10] On-Chip Training of Recurrent Neural Networks with Limited Numerical Precision
• [C9] Adaptive Weight Compression for Memory-Efficient Neural Networks
• [C8] Clock Data Compensation Aware Clock Tree Synthesis in Digital Circuits with Adaptive Clock Generation
• [C7] A Low-Power Wireless Image Sensor Node with Noise-Robust Moving Object Detection and a Region-of-Interest Based Rate Controller
• [C6] Reconfigurable 96x128 Active Pixel Sensor with 2.1μW/mm2 Power Generation and Regulated Multi- Domain Power Delivery for Self-Powered Imaging
• [C5] An Energy-Efficient Wireless Video Sensor Node with a Region-of-Interest Based Multi-Parameter Rate Controller for Moving Object Surveillance