Dorit Merhof

Achieving a 0.20% accuracy loss while eliminating BRAM usage could revolutionize the deployment of Vision Transformers on resource-constrained edge devices.

Achieving 80% higher throughput with dynamic precision in ultrasound beamforming could revolutionize real-time imaging applications on FPGA devices.

Achieving up to 82% higher energy efficiency while maintaining accuracy, MINT redefines the boundaries of CNN inference on FPGA platforms.

Merging multiplication and addition in FPGA architectures can boost energy efficiency for image segmentation tasks by up to 9x compared to existing methods.

Achieve state-of-the-art medical image segmentation accuracy with a linear-time transformer decoder that overcomes the limitations of standard linear attention by subtracting complementary attention paths to amplify relevant context.