A Flexible Design Automation Tool for Accelerating Quantized Spectral CNNs. - Rachit Rajat; Hanqing Zeng; Viktor K. Prasanna (FPL, 2019)

IP Cores for Graph Kernels on FPGAs. - Sanmukh R. Kuppannagari; Rachit Rajat; Rajgopal Kannan; Aravind Dasu; Viktor K. Prasanna (HPEC, 2019)

QTAccel: A Generic FPGA based Design for Q-Table based Reinforcement Learning Accelerators. - Rachit Rajat; Yuan Meng 001; Sanmukh R. Kuppannagari; Ajitesh Srivastava; Viktor K. Prasanna; Rajgopal Kannan (FPGA, 2020)

Look Ahead ORAM: Obfuscating Addresses in Recommendation Model Training. - Rachit Rajat; Yongqin Wang; Murali Annavaram (CoRR, 2021)

PageORAM: An Efficient DRAM Page Aware ORAM Strategy. - Rachit Rajat; Yongqin Wang; Murali Annavaram (MICRO, 2022)

MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference. - Yongqin Wang; Rachit Rajat; Murali Annavaram (CoRR, 2022)

LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables. - Rachit Rajat; Yongqin Wang; Murali Annavaram (ISCA, 2023)

MPC-Pipe: an Efficient Pipeline Scheme for Semi-honest MPC Machine Learning. - Yongqin Wang; Rachit Rajat; Murali Annavaram (ASPLOS (4), 2024)

Fastrack: Fast IO for Secure ML using GPU TEEs. - Yongqin Wang; Rachit Rajat; Jonghyun Lee; Tingting Tang; Murali Annavaram (CoRR, 2024)

PIGEON: A Framework for Private Inference of Neural Networks. - Christopher Harth-Kitzerow; Yongqin Wang; Rachit Rajat; Georg Carle; Murali Annavaram (IACR Cryptol. ePrint Arch., 2024)

PIGEON: A High Throughput Framework for Private Inference of Neural Networks using Secure Multiparty Computation. - Christopher Harth-Kitzerow; Yongqin Wang; Rachit Rajat; Georg Carle; Murali Annavaram (Proc. Priv. Enhancing Technol., 2025)

Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training. - Jonghytun Lee; Yongqin Wang; Rachit Rajat; Murali Annavaram (CoRR, 2025)