Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training
Xi Chen,
Chang Gao,
Zuowen Wang
et al.
Abstract:Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large number of arithmetic operations and memory accesses. Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware. Inspired by the spiking neuron model, the Delta RNN exploits temporal sparsity during inference by skipping over the update of hidden states from those inactiva… Show more
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