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ORCIDAndreas Moshovos
- > Home > Persons > Andreas Moshovos
Publications
- 2022
[c95]Ali Hadi Zadeh
, Mostafa Mahmoud, Ameer Abdelhadi, Andreas Moshovos:
Mokey: enabling narrow fixed-point inference for out-of-the-box floating-point transformer models. ISCA 2022: 888-901- [i18]Alberto Delmas Lascorz, Mostafa Mahmoud, Andreas Moshovos:
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference. CoRR abs/2201.08830 (2022) - [i17]Ali Hadi Zadeh, Mostafa Mahmoud, Ameer Abdelhadi, Andreas Moshovos:
Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models. CoRR abs/2203.12758 (2022) - [i16]Milos Nikolic, Enrique Torres-Sánchez, Jiahui Wang, Ali Hadi Zadeh, Mostafa Mahmoud, Ameer Abdelhadi, Andreas Moshovos:
Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training. CoRR abs/2204.13666 (2022) - 2021
- [c92]Omar Mohamed Awad, Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh, Ciaran Bannon, Anand Jayarajan, Gennady Pekhimenko, Andreas Moshovos:
FPRaker: A Processing Element For Accelerating Neural Network Training. MICRO 2021: 857-869 - [c91]Isak Edo Vivancos, Sayeh Sharify, Daniel Ly-Ma, Ameer Abdelhadi, Ciaran Bannon, Milos Nikolic, Mostafa Mahmoud, Alberto Delmas Lascorz, Gennady Pekhimenko, Andreas Moshovos:
Boveda: Building an On-Chip Deep Learning Memory Hierarchy Brick by Brick. MLSys 2021 - 2020
- [c90]Isak Edo Vivancos, Sayeh Sharify, Milos Nikolic, Ciaran Bannon, Mostafa Mahmoud, Alberto Delmas Lascorz, Andreas Moshovos:
Late Breaking Results: Building an On-Chip Deep Learning Memory Hierarchy Brick by Brick. DAC 2020: 1-2 - [c89]Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh, Omar Mohamed Awad, Gennady Pekhimenko, Jorge Albericio, Andreas Moshovos:
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training. MICRO 2020: 781-795 - [i13]Mostafa Mahmoud, Isak Edo Vivancos, Ali Hadi Zadeh, Omar Mohamed Awad, Gennady Pekhimenko, Jorge Albericio, Andreas Moshovos:
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference. CoRR abs/2009.00748 (2020) - [i12]Omar Mohamed Awad, Mostafa Mahmoud, Isak Edo Vivancos, Ali Hadi Zadeh, Ciaran Bannon, Anand Jayarajan, Gennady Pekhimenko, Andreas Moshovos:
FPRaker: A Processing Element For Accelerating Neural Network Training. CoRR abs/2010.08065 (2020) - 2019
- [j21]Mostafa Mahmoud, Dylan Malone Stuart, Zissis Poulos, Alberto Delmas Lascorz, Patrick Judd, Sayeh Sharify, Milos Nikolic, Kevin Siu, Isak Edo Vivancos, Jorge Albericio, Andreas Moshovos:
Accelerating Image-Sensor-Based Deep Learning Applications. IEEE Micro 39(5): 26-35 (2019) - [c87]Alberto Delmas Lascorz, Patrick Judd, Dylan Malone Stuart, Zissis Poulos, Mostafa Mahmoud, Sayeh Sharify, Milos Nikolic, Kevin Siu, Andreas Moshovos:
Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks. ASPLOS 2019: 749-763 - [c82]Sayeh Sharify, Alberto Delmas Lascorz, Mostafa Mahmoud, Milos Nikolic, Kevin Siu, Dylan Malone Stuart, Zissis Poulos, Andreas Moshovos:
Laconic deep learning inference acceleration. ISCA 2019: 304-317 - [c81]Milos Nikolic, Mostafa Mahmoud, Andreas Moshovos, Yiren Zhao, Robert D. Mullins:
Characterizing Sources of Ineffectual Computations in Deep Learning Networks. ISPASS 2019: 165-176 - [c80]Alberto Delmas Lascorz, Sayeh Sharify, Isak Edo Vivancos, Dylan Malone Stuart, Omar Mohamed Awad, Patrick Judd, Mostafa Mahmoud, Milos Nikolic, Kevin Siu, Zissis Poulos, Andreas Moshovos:
ShapeShifter: Enabling Fine-Grain Data Width Adaptation in Deep Learning. MICRO 2019: 28-41 - 2018
- [c77]Milos Nikolic, Mostafa Mahmoud, Andreas Moshovos:
Characterizing Sources of Ineffectual Computations in Deep Learning Networks. IISWC 2018: 86-87 - [c76]Kevin Siu, Dylan Malone Stuart, Mostafa Mahmoud, Andreas Moshovos:
Memory Requirements for Convolutional Neural Network Hardware Accelerators. IISWC 2018: 111-121 - [c74]Mostafa Mahmoud, Kevin Siu, Andreas Moshovos:
Diffy: a Déjà vu-Free Differential Deep Neural Network Accelerator. MICRO 2018: 134-147 - [c73]Andreas Moshovos, Jorge Albericio, Patrick Judd, Alberto Delmas, Sayeh Sharify, Mostafa Mahmoud, Tayler H. Hetherington, Milos Nikolic, Dylan Malone Stuart, Kevin Siu, Zissis Poulos, Tor M. Aamodt, Natalie D. Enright Jerger:
Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning. NEWCAS 2018: 356-360 - [i10]Alberto Delmas, Patrick Judd, Dylan Malone Stuart, Zissis Poulos, Mostafa Mahmoud, Sayeh Sharify, Milos Nikolic, Andreas Moshovos:
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How. CoRR abs/1803.03688 (2018) - [i8]Sayeh Sharify, Mostafa Mahmoud, Alberto Delmas Lascorz, Milos Nikolic, Andreas Moshovos:
Laconic Deep Learning Computing. CoRR abs/1805.04513 (2018) - 2017
- [c71]Mostafa Mahmoud, Bojian Zheng, Alberto Delmas Lascorz, Felix Heide, Jonathan Assouline, Paul Boucher, Emmanuel Onzon, Andreas Moshovos:
IDEAL: image denoising accelerator. MICRO 2017: 82-95 - 2016
- [c68]Mostafa Mahmoud, Andreas Moshovos:
Memory controller design under cloud workloads. IISWC 2016: 188-198 - [i2]Mostafa Mahmoud, Andreas Moshovos:
Memory Controller Design Under Cloud Workloads. CoRR abs/1611.10316 (2016)
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