Playing Board Games with Deep Convolutional Neural Network on 8bit Motorola 6809 (ipsj.ixsq.nii.ac.jp)

A recent study has demonstrated the potential of deep learning on low-power hardware by successfully running a convolutional neural network (CNN) for playing Go on the 8-bit Motorola 6809 microprocessor. This innovative implementation, carried out on a Thomson MO5 microcomputer, achieved a level of play comparable to that of GNU Go, showcasing the CNN's impressive inference capabilities despite the severely limited computational resources typical of such legacy systems. This research is significant for the AI and machine learning community as it highlights the feasibility of deploying advanced neural network models on minimal hardware, challenging the common perception that only powerful processors are suitable for AI applications. The endeavor also emphasizes techniques such as quantization, which allows for the efficient operation of deep learning models in environments with stringent resource constraints. Such advancements might open the door to a broader range of applications for AI in embedded systems and other low-power contexts, potentially revolutionizing how AI can be integrated into everyday devices.