Recently, deep reinforcement learning (DRL) algorithms have been adapted for real-time control and policy-based decision for robots, drones, and autonomous vehicles. Traditional implementations of DRL use general purpose computing (CPU) and graphical processing units (GPU). In current work, we present HardCompress as an optimized hardware configuration for neural network (DNN) accelerators using High Level Synthesis (HLS) techniques.
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