Training and Inference using Approximate Floating-Point Arithmetic for Energy Efficient Spiking Neural Network Processors. - Myeongjin Kwak; Jungwon Lee; Hyoju Seo; Mingyu Sung; Yongtae Kim (ICEIC, 2021)

Precision Exploration of Floating-Point Arithmetic for Spiking Neural Networks. - Myeongjin Kwak; Hyoju Seo; Yongtae Kim (ISOCC, 2021)

Do Not Forget: Exploiting Stability-Plasticity Dilemma to Expedite Unsupervised SNN Training for Neuromorphic Processors. - Myeongjin Kwak; Yongtae Kim (ICCD, 2022)

TorchAxf: Enabling Rapid Simulation of Approximate DNN Models Using GPU-Based Floating-Point Computing Framework. - Myeongjin Kwak; Jeonggeun Kim; Yongtae Kim (MASCOTS, 2023)

A comprehensive exploration of approximate DNN models with a novel floating-point simulation framework. - Myeongjin Kwak; Jeonggeun Kim; Yongtae Kim (Perform. Evaluation, 2024)