A. Sumarudin

An Efficient Fully Hardware DQN Accelerator With Parallel Q and Target Networks for Low-Latency Edge AI Application

Deep reinforcement learning (DRL) workloads such as Deep Q-Networks (DQNs) require repetitive forward evaluations of both the Q-network and the target network, which leads to high training latency and inefficient resource utilization on edge System-on-Chip (SoC) platforms. This paper presents an efficient parallel doub...