Design tradeoffs for hard and soft FPGA-based Networks-on-Chip. - Mohamed S. Abdelfattah; Vaughn Betz (FPT, 2012)

The power of communication: Energy-efficient NOCS for FPGAS. - Mohamed S. Abdelfattah; Vaughn Betz (FPL, 2013)

Gzip on a chip: high performance lossless data compression on FPGAs using OpenCL. - Mohamed S. Abdelfattah; Andrei Hagiescu; Deshanand P. Singh (IWOCL, 2014)

The Case for Embedded Networks on Chip on Field-Programmable Gate Arrays. - Mohamed S. Abdelfattah; Vaughn Betz (IEEE Micro, 2014)

Networks-on-Chip for FPGAs: Hard, Soft or Mixed? - Mohamed S. Abdelfattah; Vaughn Betz (ACM Trans. Reconfigurable Technol. Syst., 2014)

Take the Highway: Design for Embedded NoCs on FPGAs. - Mohamed S. Abdelfattah; Andrew Bitar; Vaughn Betz (FPGA, 2015)

Design and simulation tools for Embedded NOCs on FPGAs. - Mohamed S. Abdelfattah; Andrew Bitar; Ange Yaghi; Vaughn Betz (FPL, 2015)

Bringing programmability to the data plane: Packet processing with a NoC-enhanced FPGA. - Andrew Bitar; Mohamed S. Abdelfattah; Vaughn Betz (FPT, 2015)

LYNX: CAD for FPGA-based networks-on-chip. - Mohamed S. Abdelfattah; Vaughn Betz (FPL, 2016)

Power Analysis of Embedded NoCs on FPGAs and Comparison With Custom Buses. - Mohamed S. Abdelfattah; Vaughn Betz (IEEE Trans. Very Large Scale Integr. Syst., 2016)

Design and Applications for Embedded Networks-on-Chip on FPGAs. - Mohamed S. Abdelfattah; Andrew Bitar; Vaughn Betz (IEEE Trans. Computers, 2017)

DLA: Compiler and FPGA Overlay for Neural Network Inference Acceleration. - Mohamed S. Abdelfattah; David Han; Andrew Bitar; Roberto DiCecco; Shane O'Connell; Nitika Shanker; Joseph Chu; Ian Prins; Joshua Fender; Andrew C. Ling; Gordon R. Chiu (FPL, 2018)

Harnessing Numerical Flexibility for Deep Learning on FPGAs. - Andrew C. Ling; Mohamed S. Abdelfattah; Shane O'Connell; Andrew Bitar; David Han; Roberto DiCecco; Suchit Subhaschandra; Chris N. Johnson; Dmitry Denisenko; Joshua Fender; Gordon R. Chiu (HEART, 2018)

Flexibility: FPGAs and CAD in Deep Learning Acceleration. - Gordon R. Chiu; Andrew C. Ling; Davor Capalija; Andrew Bitar; Mohamed S. Abdelfattah (ISPD, 2018)

ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning. - Lukasz Dudziak; Mohamed S. Abdelfattah; Ravichander Vipperla; Stefanos Laskaridis; Nicholas D. Lane (INTERSPEECH, 2019)

Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator. - Mohamed S. Abdelfattah; Lukasz Dudziak; Thomas Chau 001; Royson Lee; Hyeji Kim; Nicholas D. Lane (DAC, 2020)

Journey Towards Tiny Perceptual Super-Resolution. - Royson Lee; Lukasz Dudziak; Mohamed S. Abdelfattah; Stylianos I. Venieris; Hyeji Kim; Hongkai Wen 001; Nicholas D. Lane (ECCV (26), 2020)

Codesign-NAS: Automatic FPGA/CNN Codesign Using Neural Architecture Search. - Mohamed S. Abdelfattah; Lukasz Dudziak; Thomas Chau 001; Royson Lee; Hyeji Kim; Nicholas D. Lane (FPGA, 2020)

Iterative Compression of End-to-End ASR Model Using AutoML. - Abhinav Mehrotra; Lukasz Dudziak; Jinsu Yeo; Young-Yoon Lee; Ravichander Vipperla; Mohamed S. Abdelfattah; Sourav Bhattacharya; Samin Ishtiaq; Alberto Gil C. P. Ramos; SangJeong Lee; Daehyun Kim; Nicholas D. Lane (INTERSPEECH, 2020)

BRP-NAS: Prediction-based NAS using GCNs. - Lukasz Dudziak; Thomas Chau 001; Mohamed S. Abdelfattah; Royson Lee; Hyeji Kim; Nicholas D. Lane (NeurIPS, 2020)

Temporal Kernel Consistency for Blind Video Super-Resolution. - Lichuan Xiang; Royson Lee; Mohamed S. Abdelfattah; Nicholas D. Lane; Hongkai Wen 001 (ICCVW, 2021)

Zero-Cost Proxies for Lightweight NAS. - Mohamed S. Abdelfattah; Abhinav Mehrotra; Lukasz Dudziak; Nicholas Donald Lane (ICLR, 2021)

NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition. - Abhinav Mehrotra; Alberto Gil C. P. Ramos; Sourav Bhattacharya; Lukasz Dudziak; Ravichander Vipperla; Thomas Chau 001; Mohamed S. Abdelfattah; Samin Ishtiaq; Nicholas Donald Lane (ICLR, 2021)

Are Mobile DNN Accelerators Accelerating DNNs? - Qingqing Cao; Alexandru Eugen Irimiea; Mohamed S. Abdelfattah; Aruna Balasubramanian; Nicholas D. Lane (EMDL@MobiSys, 2021)

Zero-Cost Proxies Meet Differentiable Architecture Search. - Lichuan Xiang; Lukasz Dudziak; Mohamed S. Abdelfattah; Thomas Chau 001; Nicholas D. Lane; Hongkai Wen 001 (CoRR, 2021)

Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference. - Erwei Wang; James J. Davis 001; Georgios-Ilias Stavrou; Peter Y. K. Cheung; George A. Constantinides; Mohamed S. Abdelfattah (FPGA, 2022)

Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design. - Hongxiang Fan; Thomas Chau 001; Stylianos I. Venieris; Royson Lee; Alexandros Kouris; Wayne Luk; Nicholas D. Lane; Mohamed S. Abdelfattah (MICRO, 2022)

BLOX: Macro Neural Architecture Search Benchmark and Algorithms. - Thomas Chau 001; Lukasz Dudziak; Hongkai Wen 001; Nicholas D. Lane; Mohamed S. Abdelfattah (NeurIPS, 2022)

Zero-Cost Operation Scoring in Differentiable Architecture Search. - Lichuan Xiang; Lukasz Dudziak; Mohamed S. Abdelfattah; Thomas C. P. Chau; Nicholas D. Lane; Hongkai Wen 001 (AAAI, 2023)

Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search. - Yash Akhauri; Mohamed S. Abdelfattah (AutoML, 2023)

BRAMAC: Compute-in-BRAM Architectures for Multiply-Accumulate on FPGAs. - Yuzong Chen 001; Mohamed S. Abdelfattah (FCCM, 2023)

M4BRAM: Mixed-Precision Matrix-Matrix Multiplication in FPGA Block RAMs. - Yuzong Chen 001; Jordan Dotzel; Mohamed S. Abdelfattah (ICFPT, 2023)

DiviML: A Module-based Heuristic for Mapping Neural Networks onto Heterogeneous Platforms. - Yassine Ghannane; Mohamed S. Abdelfattah (ICCAD, 2023)

Are We There Yet? Product Quantization and its Hardware Acceleration. - Javier Fernández-Marqués; Ahmed F. AbouElhamayed; Nicholas D. Lane; Mohamed S. Abdelfattah (CoRR, 2023)

Logic Shrinkage: Learned Connectivity Sparsification for LUT-Based Neural Networks. - Erwei Wang; Marie Auffret; Georgios-Ilias Stavrou; Peter Y. K. Cheung; George A. Constantinides; Mohamed S. Abdelfattah; James J. Davis 001 (ACM Trans. Reconfigurable Technol. Syst., 2023)

FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search. - Jordan Dotzel; Gang Wu; Andrew Li; Muhammad Umar 002; Yun Ni; Mohamed S. Abdelfattah; Zhiru Zhang; Liqun Cheng; Martin G. Dixon; Norman P. Jouppi; Quoc V. Le; Sheng Li 007 (AutoML, 2024)

Beyond Inference: Performance Analysis of DNN Server Overheads for Computer Vision. - Ahmed F. AbouElhamayed; Susanne Balle; Deshanand P. Singh; Mohamed S. Abdelfattah (DAC, 2024)

ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models. - Yash Akhauri; Ahmed F. AbouElhamayed; Jordan Dotzel; Zhiru Zhang; Alexander M. Rush; Safeen Huda; Mohamed S. Abdelfattah (EMNLP, 2024)

FPGA Benchmark for Unrolled DNNs with Fine-Grained Sparsity and Mixed Precision. - Xilai Dai; Yuzong Chen 001; Mohamed S. Abdelfattah (FCCM, 2024)

Kratos: An FPGA Benchmark for Unrolled DNNs with Fine-Grained Sparsity and Mixed Precision. - Xilai Dai; Yuzong Chen 001; Mohamed S. Abdelfattah (FPL, 2024)

Exploring the Limits of Semantic Image Compression at Micro-bits per Pixel. - Jordan Dotzel; Bahaa Kotb; James Dotzel; Mohamed S. Abdelfattah; Zhiru Zhang (Tiny Papers @ ICLR, 2024)

Encodings for Prediction-based Neural Architecture Search. - Yash Akhauri; Mohamed S. Abdelfattah (ICML, 2024)

Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs. - Jordan Dotzel; Yuzong Chen 001; Bahaa Kotb; Sushma Prasad; Gang Wu; Sheng Li 007; Mohamed S. Abdelfattah; Zhiru Zhang (ICML, 2024)

Towards Neural Architecture Search through Hierarchical Generative Modeling. - Lichuan Xiang; Lukasz Dudziak; Mohamed S. Abdelfattah; Abhinav Mehrotra; Nicholas Donald Lane; Hongkai Wen 001 (ICML, 2024)

BBS: Bi-Directional Bit-Level Sparsity for Deep Learning Acceleration. - Yuzong Chen 001; Jian Meng; Jae-sun Seo; Mohamed S. Abdelfattah (MICRO, 2024)

On Latency Predictors for Neural Architecture Search. - Yash Akhauri; Mohamed S. Abdelfattah (MLSys, 2024)

Fast Inference Through The Reuse Of Attention Maps In Diffusion Models. - Rosco Hunter; Lukasz Dudziak; Mohamed S. Abdelfattah; Abhinav Mehrotra; Sourav Bhattacharya; Hongkai Wen 001 (CoRR, 2024)

Radial Networks: Dynamic Layer Routing for High-Performance Large Language Models. - Jordan Dotzel; Yash Akhauri; Ahmed S. AbouElhamayed; Carly Jiang; Mohamed S. Abdelfattah; Zhiru Zhang (CoRR, 2024)

Attamba: Attending To Multi-Token States. - Yash Akhauri; Safeen Huda; Mohamed S. Abdelfattah (CoRR, 2024)

NITRO: LLM Inference on Intel Laptop NPUs. - Anthony Fei; Mohamed S. Abdelfattah (CoRR, 2024)

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration. - Yuzong Chen 001; Ahmed F. AbouElhamayed; Xilai Dai; Yang Wang; Marta Andronic; George A. Constantinides; Mohamed S. Abdelfattah (HPCA, 2025)

Palu: KV-Cache Compression with Low-Rank Projection. - Chi-Chih Chang; Wei-Cheng Lin; Chien-Yu Lin; Chong-Yan Chen; Yu-Fang Hu; Pei-Shuo Wang; Ning-Chi Huang; Luis Ceze; Mohamed S. Abdelfattah; Kai-Chiang Wu (ICLR, 2025)

PQA: Exploring the Potential of Product Quantization in DNN Hardware Acceleration. - Ahmed F. AbouElhamayed; Angela Cui; Javier Fernández-Marqués; Nicholas D. Lane; Mohamed S. Abdelfattah (ACM Trans. Reconfigurable Technol. Syst., 2025)

The Power of Negative Zero: Datatype Customization for Quantized Large Language Models. - Yuzong Chen 001; Xilai Dai; Chi-chih Chang; Yash Akhauri; Mohamed S. Abdelfattah (CoRR, 2025)

SparAMX: Accelerating Compressed LLMs Token Generation on AMX-powered CPUs. - Ahmed F. AbouElhamayed; Jordan Dotzel; Yash Akhauri; Chi-Chih Chang; Sameh Gobriel; J. Pablo Muñoz; Vui Seng Chua; Nilesh Jain; Mohamed S. Abdelfattah (CoRR, 2025)

TokenButler: Token Importance is Predictable. - Yash Akhauri; Ahmed F. AbouElhamayed; Yifei Gao; Chi-Chih Chang; Nilesh Jain; Mohamed S. Abdelfattah (CoRR, 2025)

xKV: Cross-Layer SVD for KV-Cache Compression. - Chi-Chih Chang; Chien-Yu Lin; Yash Akhauri; Wei-Cheng Lin; Kai-Chiang Wu; Luis Ceze; Mohamed S. Abdelfattah (CoRR, 2025)

Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models. - Hung-Yueh Chiang; Chi-Chih Chang; Natalia Frumkin; Kai-Chiang Wu; Mohamed S. Abdelfattah; Diana Marculescu (CoRR, 2025)

SplitReason: Learning To Offload Reasoning. - Yash Akhauri; Anthony Fei; Chi-Chih Chang; Ahmed F. AbouElhamayed; Yueying Li; Mohamed S. Abdelfattah (CoRR, 2025)

Semantic Compression of 3D Objects for Open and Collaborative Virtual Worlds. - Jordan Dotzel; Tony Montes; Mohamed S. Abdelfattah; Zhiru Zhang (CoRR, 2025)

Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion. - Zhanqiu Hu; Jian Meng; Yash Akhauri; Mohamed S. Abdelfattah; Jae-sun Seo; Zhiru Zhang; Udit Gupta (CoRR, 2025)

Performance Prediction for Large Systems via Text-to-Text Regression. - Yash Akhauri; Bryan Lewandowski; Cheng-Hsi Lin; Adrian N. Reyes; Grant C. Forbes; Arissa Wongpanich; Bangding Yang; Mohamed S. Abdelfattah; Sagi Perel; Xingyou Song (CoRR, 2025)

Hybrid Systolic Array Accelerator with Optimized Dataflow for Edge Large Language Model Inference. - Chun-Ting Chen; HanGyeol Mun; Jian Meng; Mohamed S. Abdelfattah; Jae-sun Seo (CoRR, 2025)

Double Duty: FPGA Architecture to Enable Concurrent LUT and Adder Chain Usage. - Junius Pun; Xilai Dai; Grace Zgheib; Mahesh A. Iyer; Andrew Boutros; Vaughn Betz; Mohamed S. Abdelfattah (CoRR, 2025)