FPGA technology mapping: a study of optimality. - Andrew C. Ling; Deshanand P. Singh; Stephen Dean Brown (DAC, 2005)

FPGA PLB Evaluation using Quantified Boolean Satisfiability. - Andrew C. Ling; Deshanand P. Singh; Stephen Dean Brown (FPL, 2005)

FPGA Logic Synthesis Using Quantified Boolean Satisfiability. - Andrew C. Ling; Deshanand P. Singh; Stephen Dean Brown (SAT, 2005)

BddCut: Towards Scalable Symbolic Cut Enumeration. - Andrew C. Ling; Jianwen Zhu; Stephen Dean Brown (ASP-DAC, 2007)

Incremental placement for structured ASICs using the transportation problem. - Andrew C. Ling; Deshanand P. Singh; Stephen Dean Brown (VLSI-SoC, 2007)

FPGA PLB Architecture Evaluation and Area Optimization Techniques Using Boolean Satisfiability. - Andrew C. Ling; Deshanand P. Singh; Stephen Dean Brown (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2007)

Delay driven AIG restructuring using slack budget management. - Andrew C. Ling; Jianwen Zhu; Stephen Dean Brown (ACM Great Lakes Symposium on VLSI, 2008)

Scalable Synthesis and Clustering Techniques Using Decision Diagrams. - Andrew C. Ling; Jianwen Zhu; Stephen Dean Brown (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2008)

Towards automated ECOs in FPGAs. - Andrew C. Ling; Stephen Dean Brown; Jianwen Zhu; Sean Safarpour (FPGA, 2009)

Towards scalable placement for FPGAs. - Huimin Bian; Andrew C. Ling; Alexander Choong; Jianwen Zhu (FPGA, 2010)

Toward Automated ECOs in FPGAs. - Andrew C. Ling; Stephen Dean Brown; Sean Safarpour; Jianwen Zhu (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2011)

Harnessing the power of FPGAs using altera's OpenCL compiler. - Deshanand P. Singh; Tomasz S. Czajkowski; Andrew C. Ling (FPGA, 2013)

An OpenCL™ Deep Learning Accelerator on Arria 10. - Utku Aydonat; Shane O'Connell; Davor Capalija; Andrew C. Ling; Gordon R. Chiu (FPGA, 2017)

Customizable FPGA OpenCL matrix multiply design template for deep neural networks. - Jack Yinger; Eriko Nurvitadhi; Davor Capalija; Andrew C. Ling; Debbie Marr; Krishnan Srivatsan; Duncan J. M. Moss; Suchit Subhaschandra (FPT, 2017)

Creating High Performance Applications with Intel's FPGA OpenCL™ SDK. - Andrew C. Ling; Utku Aydonat; Shane O'Connell; Davor Capalija; Gordon R. Chiu (IWOCL, 2017)

Harnessing the Power of FPGAs with the Intel FPGA SDK for OpenCL™. - Byron Sinclair; Andrew C. Ling; Genady Paikin (IWOCL, 2017)

In-Package Domain-Specific ASICs for Intel® Stratix® 10 FPGAs: A Case Study of Accelerating Deep Learning Using TensorTile ASIC(Abstract Only). - Eriko Nurvitadhi; Jeffrey J. Cook; Asit K. Mishra; Debbie Marr; Kevin Nealis; Philip Colangelo; Andrew C. Ling; Davor Capalija; Utku Aydonat; Sergey Y. Shumarayev; Aravind Dasu (FPGA, 2018)

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)

In-Package Domain-Specific ASICs for Intel® Stratix® 10 FPGAs: A Case Study of Accelerating Deep Learning Using TensorTile ASIC. - Eriko Nurvitadhi; Jeffrey J. Cook; Asit K. Mishra; Debbie Marr; Kevin Nealis; Philip Colangelo; Andrew C. Ling; Davor Capalija; Utku Aydonat; Aravind Dasu; Sergey Y. Shumarayev (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)

Challenges/Opportunities to Enable Dependable Scale-out System with Groq Deterministic Tensor-Streaming Processors. - Dennis Abts; Ibrahim Ahmed 007; Andrew Bitar; Matthew Boyd; John Kim 001; Garrin Kimmell; Andrew C. Ling (DSN (Supplements), 2022)

The Groq Software-defined Scale-out Tensor Streaming Multiprocessor : From chips-to-systems architectural overview. - Dennis Abts; John Kim 001; Garrin Kimmell; Matthew Boyd; Kris Kang; Sahil Parmar; Andrew C. Ling; Andrew Bitar; Ibrahim Ahmed 007; Jonathan Ross (HCS, 2022)

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)