The RACECAR heuristic for automatic function specialization on multi-core heterogeneous systems. - John Robert Wernsing; Greg Stitt; Jeremy Fowers (CASES, 2012)

A performance and energy comparison of FPGAs, GPUs, and multicores for sliding-window applications. - Jeremy Fowers; Greg Brown; Patrick Cooke; Greg Stitt (FPGA, 2012)

A comparison of correntropy-based feature tracking on FPGAs and GPUs. - Patrick Cooke; Jeremy Fowers; Greg Stitt; Lee Hunt (ASAP, 2013)

A high-performance, low-energy FPGA accelerator for correntropy-based feature tracking (abstract only). - Patrick Cooke; Jeremy Fowers; Lee Hunt; Greg Stitt (FPGA, 2013)

Dynafuse: dynamic dependence analysis for FPGA pipeline fusion and locality optimizations. - Jeremy Fowers; Greg Stitt (FPGA, 2013)

A performance and energy comparison of convolution on GPUs, FPGAs, and multicore processors. - Jeremy Fowers; Greg Brown; John Robert Wernsing; Greg Stitt (ACM Trans. Archit. Code Optim., 2013)

A High Memory Bandwidth FPGA Accelerator for Sparse Matrix-Vector Multiplication. - Jeremy Fowers; Kalin Ovtcharov; Karin Strauss; Eric S. Chung; Greg Stitt (FCCM, 2014)

A framework for dynamic parallelization of FPGA-accelerated applications. - Jeremy Fowers; Jianye Liu; Greg Stitt (SCOPES, 2014)

A Scalable High-Bandwidth Architecture for Lossless Compression on FPGAs. - Jeremy Fowers; Joo-Young Kim 001; Doug Burger; Scott Hauck (FCCM, 2015)

Toward accelerating deep learning at scale using specialized hardware in the datacenter. - Kalin Ovtcharov; Olatunji Ruwase; Joo-Young Kim 001; Jeremy Fowers; Karin Strauss; Eric S. Chung (Hot Chips Symposium, 2015)

A Tradeoff Analysis of FPGAs, GPUs, and Multicores for Sliding-Window Applications. - Patrick Cooke; Jeremy Fowers; Greg Brown; Greg Stitt (ACM Trans. Reconfigurable Technol. Syst., 2015)

A cloud-scale acceleration architecture. - Adrian M. Caulfield; Eric S. Chung; Andrew Putnam; Hari Angepat; Jeremy Fowers; Michael Haselman; Stephen Heil; Matt Humphrey; Puneet Kaur; Joo-Young Kim 001; Daniel Lo; Todd Massengill; et al. (MICRO, 2016)

A reconfigurable fabric for accelerating large-scale datacenter services. - Andrew Putnam; Adrian M. Caulfield; Eric S. Chung; Derek Chiou; Kypros Constantinides; John Demme; Hadi Esmaeilzadeh; Jeremy Fowers; Gopi Prashanth Gopal; Jan Gray; Michael Haselman; Scott Hauck; et al. (Commun. ACM, 2016)

Configurable Clouds. - Adrian M. Caulfield; Eric S. Chung; Andrew Putnam; Hari Angepat; Daniel Firestone; Jeremy Fowers; Michael Haselman; Stephen Heil; Matt Humphrey; Puneet Kaur; Joo-Young Kim 001; Daniel Lo; et al. (IEEE Micro, 2017)

A Configurable Cloud-Scale DNN Processor for Real-Time AI. - Jeremy Fowers; Kalin Ovtcharov; Michael Papamichael; Todd Massengill; Ming Liu; Daniel Lo; Shlomi Alkalay; Michael Haselman; Logan Adams; Mahdi Ghandi; Stephen Heil; Prerak Patel; et al. (ISCA, 2018)

Serving DNNs in Real Time at Datacenter Scale with Project Brainwave. - Eric S. Chung; Jeremy Fowers; Kalin Ovtcharov; Michael Papamichael; Adrian M. Caulfield; Todd Massengill; Ming Liu; Daniel Lo; Shlomi Alkalay; Michael Haselman; Maleen Abeydeera; Logan Adams; et al. (IEEE Micro, 2018)

Inside Project Brainwave's Cloud-Scale, Real-Time AI Processor. - Jeremy Fowers; Kalin Ovtcharov; Michael K. Papamichael; Todd Massengill; Ming Liu; Daniel Lo; Shlomi Alkalay; Michael Haselman; Logan Adams; Mahdi Ghandi; Stephen Heil; Prerak Patel; et al. (IEEE Micro, 2019)

Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point. - Bita Darvish Rouhani; Daniel Lo; Ritchie Zhao; Ming Liu; Jeremy Fowers; Kalin Ovtcharov; Anna Vinogradsky; Sarah Massengill; Lita Yang; Ray Bittner; Alessandro Forin; Haishan Zhu; et al. (NeurIPS, 2020)

A software-defined tensor streaming multiprocessor for large-scale machine learning. - Dennis Abts; Garrin Kimmell; Andrew C. Ling; John Kim 001; Matthew Boyd; Andrew Bitar; Sahil Parmar; Ibrahim Ahmed 007; Roberto DiCecco; David Han; John Thompson; Michael Bye; et al. (ISCA, 2022)

A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads. - Murali Emani; Zhen Xie; Siddhisanket Raskar; Varuni Sastry; William Arnold; Bruce Wilson; Rajeev Thakur; Venkatram Vishwanath; Zhengchun Liu; Michael E. Papka; Cindy Orozco Bohorquez; Rick Weisner; et al. (PMBS@SC, 2022)