Echocardiographic assessment of End-Diastolic Volume (EDV) is central to identifying dilated cardiomyopathy, a major driver of heart failure. This paper presents a low-latency, edge-computing solution that deploys an 8-bit quantized, 50%-pruned CNN directly onto a Xilinx Artix-7 FPGA via the hls4ml interface, enabling real-time, on-device classification of EDV/ventricular enlargement from echocardiogram frames without relying on cloud infrastructure. The approach is designed for integration into portable ultrasound devices to support point-of-care heart failure screening in emergency departments and resource-limited clinics. This work was presented at the 5th Serbian International Conference on Applied Artificial Intelligence (SICAAI 2026), Kragujevac, Serbia, and was carried out within the STRATIFYHF project.