تقرير
Method for Hybrid Precision Convolutional Neural Network Representation
| العنوان: | Method for Hybrid Precision Convolutional Neural Network Representation |
|---|---|
| المؤلفون: | Al-Hami, Mo'taz, Pietron, Marcin, Kumar, Rishi, Casas, Raul A., Hijazi, Samer L., Rowen, Chris |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Neural and Evolutionary Computing |
| الوصف: | This invention addresses fixed-point representations of convolutional neural networks (CNN) in integrated circuits. When quantizing a CNN for a practical implementation there is a trade-off between the precision used for operations between coefficients and data and the accuracy of the system. A homogenous representation may not be sufficient to achieve the best level of performance at a reasonable cost in implementation complexity or power consumption. Parsimonious ways of representing data and coefficients are needed to improve power efficiency and throughput while maintaining accuracy of a CNN. Comment: Cadence Design Systems |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1807.09760 |
| رقم الأكسشن: | edsarx.1807.09760 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |