Search Results for author: Daniel Brand

Found 4 papers, 1 papers with code

Uncovering the Data-Related Limits of Human Reasoning Research: An Analysis based on Recommender Systems

1 code implementation11 Mar 2020 Nicolas Riesterer, Daniel Brand, Marco Ragni

Understanding the fundamentals of human reasoning is central to the development of any system built to closely interact with humans.

Collaborative Filtering Recommendation Systems

Training Deep Neural Networks with 8-bit Floating Point Numbers

no code implementations NeurIPS 2018 Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan

The state-of-the-art hardware platforms for training Deep Neural Networks (DNNs) are moving from traditional single precision (32-bit) computations towards 16 bits of precision -- in large part due to the high energy efficiency and smaller bit storage associated with using reduced-precision representations.

AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training

no code implementations7 Dec 2017 Chia-Yu Chen, Jungwook Choi, Daniel Brand, Ankur Agrawal, Wei zhang, Kailash Gopalakrishnan

Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100 of TeraOps/s of computational capacity) is expected to be severely communication constrained.

Quantization

MEC: Memory-efficient Convolution for Deep Neural Network

no code implementations ICML 2017 Minsik Cho, Daniel Brand

However, all these indirect methods have high memory-overhead, which creates performance degradation and offers a poor trade-off between performance and memory consumption.

Cannot find the paper you are looking for? You can Submit a new open access paper.