Evaluating the memory system behavior of smartphone workloads. - Goran Narancic; Patrick Judd; Di Wu 006; Islam Atta; Michel Elnacouzi; Jason Zebchuk; Jorge Albericio; Natalie D. Enright Jerger; Andreas Moshovos; Kyros Kutulakos; Serag Gadelrab (ICSAMOS, 2014)

Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets. - Patrick Judd; Jorge Albericio; Tayler H. Hetherington; Tor M. Aamodt; Natalie D. Enright Jerger; Raquel Urtasun; Andreas Moshovos (CoRR, 2015)

Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks. - Patrick Judd; Jorge Albericio; Tayler H. Hetherington; Tor M. Aamodt; Natalie D. Enright Jerger; Andreas Moshovos (ICS, 2016)

Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing. - Jorge Albericio; Patrick Judd; Tayler H. Hetherington; Tor M. Aamodt; Natalie D. Enright Jerger; Andreas Moshovos (ISCA, 2016)

Stripes: Bit-serial deep neural network computing. - Patrick Judd; Jorge Albericio; Tayler H. Hetherington; Tor M. Aamodt; Andreas Moshovos (MICRO, 2016)

Bit-Pragmatic Deep Neural Network Computing. - Jorge Albericio; Patrick Judd; Alberto Delmas; Sayeh Sharify; Andreas Moshovos (ICLR (Workshop), 2017)

Bit-pragmatic deep neural network computing. - Jorge Albericio; Alberto Delmas; Patrick Judd; Sayeh Sharify; Gerard O'Leary; Roman Genov; Andreas Moshovos (MICRO, 2017)

Stripes: Bit-Serial Deep Neural Network Computing. - Patrick Judd; Jorge Albericio; Andreas Moshovos (IEEE Comput. Archit. Lett., 2017)

Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks. - Alberto Delmas; Patrick Judd; Sayeh Sharify; Andreas Moshovos (CoRR, 2017)

Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability. - Alberto Delmas; Sayeh Sharify; Patrick Judd; Andreas Moshovos (CoRR, 2017)

Cnvlutin2: Ineffectual-Activation-and-Weight-Free Deep Neural Network Computing. - Patrick Judd; Alberto Delmas Lascorz; Sayeh Sharify; Andreas Moshovos (CoRR, 2017)

Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks. - Sayeh Sharify; Alberto Delmas Lascorz; Patrick Judd; Andreas Moshovos (CoRR, 2017)

Loom: exploiting weight and activation precisions to accelerate convolutional neural networks. - Sayeh Sharify; Alberto Delmas Lascorz; Kevin Siu; Patrick Judd; Andreas Moshovos (DAC, 2018)

Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning. - Andreas Moshovos; Jorge Albericio; Patrick Judd; Alberto Delmas; Sayeh Sharify; Mostafa Mahmoud; Tayler H. Hetherington; Milos Nikolic 002; Dylan Malone Stuart; Kevin Siu; Zissis Poulos; Tor M. Aamodt; et al. (NEWCAS, 2018)

Exploiting Typical Values to Accelerate Deep Learning. - Andreas Moshovos; Jorge Albericio; Patrick Judd; Alberto Delmas Lascorz; Sayeh Sharify; Zissis Poulos; Tayler H. Hetherington; Tor M. Aamodt; Natalie D. Enright Jerger (Computer, 2018)

Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How. - Alberto Delmas; Patrick Judd; Dylan Malone Stuart; Zissis Poulos; Mostafa Mahmoud; Sayeh Sharify; Milos Nikolic 002; Andreas Moshovos (CoRR, 2018)

DPRed: Making Typical Activation Values Matter In Deep Learning Computing. - Alberto Delmas; Sayeh Sharify; Patrick Judd; Milos Nikolic 002; Andreas Moshovos (CoRR, 2018)

Value-Based Deep-Learning Acceleration. - Andreas Moshovos; Jorge Albericio; Patrick Judd; Alberto Delmas Lascorz; Sayeh Sharify; Tayler H. Hetherington; Tor M. Aamodt; Natalie D. Enright Jerger (IEEE Micro, 2018)

Proteus: Exploiting precision variability in deep neural networks. - Patrick Judd; Jorge Albericio; Tayler H. Hetherington; Tor M. Aamodt; Natalie D. Enright Jerger; Raquel Urtasun; Andreas Moshovos (Parallel Comput., 2018)

Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks. - Alberto Delmas Lascorz; Patrick Judd; Dylan Malone Stuart; Zissis Poulos; Mostafa Mahmoud; Sayeh Sharify; Milos Nikolic 002; Kevin Siu; Andreas Moshovos (ASPLOS, 2019)

ShapeShifter: Enabling Fine-Grain Data Width Adaptation in Deep Learning. - Alberto Delmas Lascorz; Sayeh Sharify; Isak Edo Vivancos; Dylan Malone Stuart; Omar Mohamed Awad; Patrick Judd; Mostafa Mahmoud; Milos Nikolic 002; Kevin Siu; Zissis Poulos; Andreas Moshovos (MICRO, 2019)

Accelerating Image-Sensor-Based Deep Learning Applications. - Mostafa Mahmoud; Dylan Malone Stuart; Zissis Poulos; Alberto Delmas Lascorz; Patrick Judd; Sayeh Sharify; Milos Nikolic 002; Kevin Siu; Isak Edo Vivancos; Jorge Albericio; Andreas Moshovos (IEEE Micro, 2019)

Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation. - Hao Wu; Patrick Judd; Xiaojie Zhang; Mikhail Isaev; Paulius Micikevicius (CoRR, 2020)

FP8 Formats for Deep Learning. - Paulius Micikevicius; Dusan Stosic; Neil Burgess; Marius Cornea; Pradeep Dubey; Richard Grisenthwaite; Sangwon Ha; Alexander Heinecke; Patrick Judd; John Kamalu; Naveen Mellempudi; Stuart F. Oberman; et al. (CoRR, 2022)