Enrico Calore

About

Enrico Calore is a researcher at the forefront of high-performance reconfigurable computing, with a focused expertise in accelerating medical image analysis using FPGA-based systems. His primary research areas include hardware acceleration for deep neural networks, semantic segmentation for medical imaging, and the deployment of AI models on edge computing platforms. Calore’s most notable contributions center on the automated detection and segmentation of aortic valve calcium lesions—a critical task in cardiovascular diagnostics. His work, including "Segmentation of Aortic Valve Calcium Lesions Using FPGA Accelerators" (2025, 2 citations) and "Benchmarking a DNN for aortic valve calcium lesions segmentation on FPGA-based DPU using the Vitis AI toolchain" (2025, 1 citation), demonstrates how convolutional neural networks can be efficiently mapped onto FPGA-based Deep Processing Units (DPUs) to achieve real-time, pixel-level classification. By leveraging the Vitis AI toolchain, Calore has shown that reconfigurable hardware can deliver the computational efficiency needed for clinical deployment without sacrificing accuracy. His research bridges the gap between advanced computer vision techniques—typically used in autonomous vehicles and robotics—and the stringent requirements of medical imaging, paving the way for faster, more reliable diagnostic tools in cardiology.

Research Focus

Key Achievements

Top Papers

1. 1
   Segmentation of Aortic Valve Calcium Lesions Using FPGA Accelerators

   2 citations · 2025
2. 2
   Benchmarking a DNN for aortic valve calcium lesions segmentation on FPGA-based DPU using the vitis AI toolchain

   1 citations · 2025

Key Collaborators

Contact & Links