4 citations · 6 across the 5 of their papers we have counts for
5 papers
Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited Learning
Juyoung Yun, Sol Choi, Francois Rameau +2
With the increasing complexity of machine learning models, managing computational resources like memory and processing power has become a critical concern. Mixed precision techniqu…
The Hidden Power of Pure 16-bit Floating-Point Neural Networks
Juyoung Yun, Byungkon Kang, Zhoulai Fu
Lowering the precision of neural networks from the prevalent 32-bit precision has long been considered harmful to performance, despite the gain in space and time. Many works propos…
Towards Efficient Data-flow Test Data Generation
Ting Su, Chengyu Zhang, Yichen Yan +5
Data-flow testing (DFT) aims to detect potential data interaction anomalies by focusing on the points at which variables receive values and the points at which these values are use…
Achieving High Coverage for Floating-point Code via Unconstrained Programming (Extended Version)
Zhoulai Fu, Zhendong Su
Achieving high code coverage is essential in testing, which gives us confidence in code quality. Testing floating-point code usually requires painstaking efforts in handling floati…
Mathematical Execution: A Unified Approach for Testing Numerical Code
Zhoulai Fu, Zhendong Su
This paper presents Mathematical Execution (ME), a new, unified approach for testing numerical code. The key idea is to (1) capture the desired testing objective via a representing…