Imperial College London > Talks@ee.imperial > CAS Talks > Redundancy in Neural Network Hardware and A Case Study of Parameter Precision
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Redundancy in Neural Network Hardware and A Case Study of Parameter PrecisionAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact George A Constantinides. Two levels of redundancy are widely found in modern neural networks. Model level and data level. As a result, hardware systems for neural networks are limited in regards to their applicability within power-constrained hardware environments due to the required high compute and memory complexity. In this talk, these two levels of redundancy will be introduced. Furthermore, a case study in data-level redundancy will be presented about parameter precision and its impacts to model accuracy as well as hardware implementation on FPGA -based digital systems. This talk is part of the CAS Talks series. This talk is included in these lists:Note that ex-directory lists are not shown. |
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