End-to-End FPGA-based Object Detection Using Pipelined CNN and Non-Maximum Suppression. - Anupreetham Anupreetham; Mohamed Ibrahim; Mathew Hall; Andrew Boutros; Ajay Kuzhively; Abinash Mohanty; Eriko Nurvitadhi; Vaughn Betz; Yu Cao 001; Jae-sun Seo (FPL, 2021)

3D-ISC: A 65nm 3D Compatible In-Sensor Computing Accelerator with Reconfigurable Tile Architecture for Real-Time DVS Data Compression. - Gokul Krishnan; Gopikrishnan Raveendran Nair; Jonghyun Oh; Anupreetham Anupreetham; Pragnya Sudershan Nalla; Ahmed Hassan; Injune Yeo; Kishore Kasichainula; Jae-sun Seo; Mingoo Seok; Yu Cao 001 (A-SSCC, 2023)

IM-SNN: Memory-Efficient Spiking Neural Network with Low-Precision Membrane Potentials and Weights. - Ahmed Hasssan; Jian Meng; Anupreetham Anupreetham; Jae-sun Seo (ICONS, 2024)

Torch2Chip: An End-to-end Customizable Deep Neural Network Compression and Deployment Toolkit for Prototype Hardware Accelerator Design. - Jian Meng; Yuan Liao; Anupreetham Anupreetham; Ahmed Hassan; Shixing Yu; Han-Sok Suh; Xiaofeng Hu; Jae-sun Seo (MLSys, 2024)

High Throughput FPGA-Based Object Detection via Algorithm-Hardware Co-Design. - Anupreetham Anupreetham; Mohamed Ibrahim; Mathew Hall; Andrew Boutros; Ajay Kuzhively; Abinash Mohanty; Eriko Nurvitadhi; Vaughn Betz; Yu Cao 001; Jae-Sun Seo (ACM Trans. Reconfigurable Technol. Syst., 2024)

Quant-NeRF: Efficient End-to-End Quantization of Neural Radiance Fields with Low-Precision 3D Gaussian Representation. - Ahmed Hasssan; Anupreetham Anupreetham; Jian Meng; Jae-sun Seo (ICASSP, 2025)