We present a real-time field-programmable gate array (FPGA) implementation of a convolutional neural network (CNN) for event classification on full calorimeter images that retain crystal-level shower information. To meet the resource and latency constraints of trigger electronics, the network parameters and hidden activations were quantized to 8 bits with quantization-aware training, and a lossless space-to-depth reorganization rearranged the input image into fewer spatial positions, reducing the estimated CNN inference latency by more than an order of magnitude. The approach was evaluated with the J-PARC KOTO detector, recorded data, and data-acquisition (DAQ) constraints as a benchmark, targeting the online separation of the rare K⁰_L→π⁰e⁺e⁻ decay from its dominant K⁰_L→π⁺π⁻π⁰ background. As a proof of principle, the firmware was implemented and validated on an AMD Versal Premium VPK120 evaluation board. On 100,000 test events, inference on the board rejected (99.34±0.04)% of the recorded background sample enriched in K⁰_L→π⁺π⁻π⁰ decays at a nominal K⁰_L→π⁰e⁺e⁻ efficiency of 95%, with a measured CNN inference latency of 17.9 μs. Combining the measured image-assembly and CNN inference latencies with the estimated link-transfer and score-return times gives a projected end-to-end latency of 25.9 μs, within the adopted 32 μs DAQ latency budget. These results demonstrate the feasibility of using full-granularity calorimeter images for CNN-based event selection that achieves high background rejection while keeping the trigger latency within tens of microseconds.