Data security and integrity are crucial across various domains, particularly in Edge-AI applications like healthcare and intelligent systems. Security concerns intensify when deploying AI at the edge due to sensitive data being processed locally rather than in cloud services. Cryptographic algorithms are usually employed on Edge-AI platforms to ensure data integrity and protect confidential data. Among the most effective approaches for developing digital signature algorithms (DSA), elliptic curve cryptography (ECC) stands out. In our research, we designed and implemented a dedicated hardware computing core for DSA using the ECDSA algorithm. This core accelerates processing throughput on FPGA-based edge-AI platforms. We implemented the proposed system on an Edge-AI platform powered by a Xilinx FPGA Zynq UltraScale+ chip called Kria KV260. Experimental results, using a dataset certified by the National Institute of Standards and Technology (NIST), demonstrate that throughput by up to 1,680 signatures per second and 1.23 Mbps have been achieved with the prototype system.