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Before we saw worldwide collaborative efforts in training machine-learning models or widespread deployments of prediction-as-a-service, we need to devise an efﬁcient privacy-preserving mechanism which guarantees the privacy of all stakeholders (data contributors, model owner, and queriers).
Using this proposed quantization method, we quantized a substantial portion of weight filters of MobileNets to ternary values resulting in 27. 98% savings in energy, and a 51. 07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets.
Since choosing the optimal bitwidths is not straight forward, training methods, which can learn them, are desirable.
Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.
Reduced precision computation is one of the key areas addressing the widening’compute gap’, driven by an exponential growth in deep learning applications.
Network quantization is a model compression and acceleration technique that has become essential to neural network deployment.
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed on clusters to perform model fitting in parallel.