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Standard Deviation-Based Quantization for Deep Neural Networks
Quantization of deep neural networks is a promising approach that reduces the inference cost, making it feasible to run deep networks on resource-restricted devices.
Training Linear Finite-State Machines
Therefore, our FSM-based model can learn extremely long-term dependencies as it requires 1/l memory storage during training compared to LSTMs, where l is the number of time steps.
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic
The emergence of XNOR networks seek to reduce the model size and computational cost of neural networks for their deployment on specialized hardware requiring real-time processes with limited hardware resources.
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