The rapid growth of artificial intelligence and the increasing complexity of neural network models are driving demand for efficient hardware architectures that can address power-constrained and ...
In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. The use of sparsity and pruning for optimizing ...
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