The NSF AI Institute for Edge Computing (Athena) is pleased to present the next in the Seminar Series by Jong Hwan Ko, Kang Eun Jeon, Johnny Rhe, and Sangheum Yeon, titled "Enabling Resource-Efficient AI via SW-HW Co-design" on Wednesday, August 13, 2025, from 3-5pm EST in-person at Duke University Wilkinson Building Room 132, and via […]

The size and complexity of recent deep learning models continue to increase exponentially, causing a serious amount of hardware overhead for training those models. Contrary to inference-only hardware, neural network training is very sensitive to computation errors; hence, training processors must support high-precision computation to avoid a large performance drop, severely limiting their processing efficiency. […]