Sang Yeob Kim
Papers
1
Total Citations
4
H-Index
1
About
Sang Yeob Kim is a leading researcher in energy-efficient deep neural network (DNN) hardware design, with a focus on enabling natural human-robot interaction (HRI) in mobile and embedded systems. His most cited work, "1b-16b Variable Bit Precision DNN Processor for Emotional HRI System in Mobile Devices" (2020), introduces a groundbreaking processor that combines a look-up-table-based processing engine (LPE) with a near-zero skipper to dramatically reduce energy consumption. This processor uniquely integrates a CNN-based facial emotion recognition model and an RNN-based emotional dialogue generation model, allowing mobile devices to perceive human emotions and respond conversationally in real time. By supporting variable bit precision from 1b to 16b, Kim's design achieves significant computational efficiency without sacrificing accuracy, a critical advancement for deploying sophisticated AI in power-constrained environments. His work has garnered attention for its practical impact on emotionally intelligent robotics, with 4 citations reflecting its niche but growing influence. Kim's contributions are paving the way for more responsive, energy-aware HRI systems that can operate seamlessly on everyday devices.
Research Focus
Key Achievements
Top Papers
- 1