Direct from Development
Direct from Development
复制标题
直接来自开发
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Ramesh Radhakrishnan
中科院分区：
文献类型：
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作者：
Matt Ogle;Ramesh Radhakrishnan
Deep learning is a class of machine learning that learns a neural network model from sample data sets over a series of training iterations and loss function [1]. The output of this phase, the learned model, is then used to make predictions on new data. While this model-learning process lends itself well to singleinstruction multiple data (SIMD) type computing with coarsegrain architectures like many-core CPUs and GPUs, the inferencing process is much more amenable to irregular, finegrain architectures like FPGAs, which allow for greater architectural flexibility to meet specific application requirements: latency, throughput, power, etc. Inferencing is the stage where most enterprises realize the business value of their AI investments.
