This paper introduces DiceTrack, an innovative Deep Learning (DL) platform for detecting dice in board games. Deploying robust models on microcontrollers (MCUs) presents challenges due to memory and computational constraints. We focus on optimizing MobileNet for seamless ESP32 deployment and propose two novel ultra-lightweight models, Separable Convolutional Layers with Quantization Network (SCLQNet) and Binarized Neural Network (BNNet), for dice classification. SCLQNet uses separable convolutional layers with quantized weights and activations, while BNNet employs a unique Binarized architecture. Further, we create DiceVision, a custom dice classification dataset tailored for real-time digital board games. Comprehensive evaluations on ESP32 and Raspberry Pi 4 showcase the efficiency of the proposed models. SCLQNet and BNNet achieve 97.5% and 97.4% accuracy with 32KB and 22KB model sizes. Notably, SCLQNet takes only 402 ms for ESP32 inference, enabling 88% latency reduction compared to quantized MobileNet.