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ORCIDSayeh Sharify
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2020 – today
- 2026
[i17]Zifei Xu, Sayeh Sharify, Hesham Mostafa:
MF-QAT: Multi-Format Quantization-Aware Training for Elastic Inference. CoRR abs/2604.00529 (2026)- [i16]Sayeh Sharify, Mahsa Salmani, Hesham Mostafa:
DiRotQ: Rotation-Aware Quantization for 4-bit Diffusion Transformers. CoRR abs/2605.16732 (2026) - 2025
- [c14]Zihao Deng, Sayeh Sharify, Xin Wang, Michael Orshansky:
Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic Programming. DAC 2025: 1-7 - [c13]Utkarsh Saxena, Sayeh Sharify, Kaushik Roy, Xin Wang:
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals. ICML 2025 - [c12]Zifei Xu, Sayeh Sharify, Wanzin Yazar, Tristan Webb, Xin Wang:
Understanding the Difficulty of Low-Precision Post-Training Quantization for LLMs. IJCNN 2025: 1-8 - [i15]Zifei Xu, Sayeh Sharify, Hesham Mostafa, Tristan Webb, Wanzin Yazar, Xin Wang:
Early Attentive Sparsification Accelerates Neural Speech Transcription. CoRR abs/2506.15912 (2025) - 2024
- [c11]Sayeh Sharify, Utkarsh Saxena, Zifei Xu, Wanzin Yazar, Ilya Soloveychik, Xin Wang:
Post Training Quantization of Large Language Models with Microscaling Formats. ENLSP 2024: 241-258 - [c10]Zifei Xu, Alexander Lan, Wanzin Yazar, Tristan Webb, Sayeh Sharify, Xin Wang:
Scaling laws for post-training quantized large language models. ENLSP 2024: 270-285 - [i14]Tian Jin, Wanzin Yazar, Zifei Xu, Sayeh Sharify, Xin Wang:
Self-Selected Attention Span for Accelerating Large Language Model Inference. CoRR abs/2404.09336 (2024) - [i13]Sayeh Sharify, Zifei Xu, Wanzin Yazar, Xin Wang:
Combining multiple post-training techniques to achieve most efficient quantized LLMs. CoRR abs/2405.07135 (2024) - [i12]Zifei Xu, Alexander Lan, Wanzin Yazar, Tristan Webb, Sayeh Sharify, Xin Wang:
Scaling laws for post-training quantized large language models. CoRR abs/2410.12119 (2024) - [i11]Zifei Xu, Sayeh Sharify, Wanzin Yazar, Tristan Webb, Xin Wang:
Understanding the difficulty of low-precision post-training quantization of large language models. CoRR abs/2410.14570 (2024) - [i10]Utkarsh Saxena, Sayeh Sharify, Kaushik Roy, Xin Wang:
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals. CoRR abs/2412.14363 (2024) - 2023
- [i9]Zihao Deng, Xin Wang, Sayeh Sharify, Michael Orshansky:
Mixed-Precision Quantization with Cross-Layer Dependencies. CoRR abs/2307.05657 (2023) - 2021
- [c9]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
- [c8]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
2010 – 2019
- 2019
- [j3]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) - [c7]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 - [c6]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 - [c5]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
- [j2]Andreas Moshovos, Jorge Albericio, Patrick Judd, Alberto Delmas Lascorz, Sayeh Sharify, Zissis Poulos, Tayler H. Hetherington, Tor M. Aamodt, Natalie D. Enright Jerger
:
Exploiting Typical Values to Accelerate Deep Learning. Computer 51(5): 18-30 (2018) - [j1]Andreas Moshovos, Jorge Albericio, Patrick Judd, Alberto Delmas Lascorz, Sayeh Sharify, Tayler H. Hetherington, Tor M. Aamodt, Natalie D. Enright Jerger:
Value-Based Deep-Learning Acceleration. IEEE Micro 38(1): 41-55 (2018) - [c4]Sayeh Sharify, Alberto Delmas Lascorz, Kevin Siu, Patrick Judd, Andreas Moshovos:
Loom: exploiting weight and activation precisions to accelerate convolutional neural networks. DAC 2018: 20:1-20:6 - [c3]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 - [i8]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) - [i7]Alberto Delmas, Sayeh Sharify, Patrick Judd, Milos Nikolic, Andreas Moshovos:
DPRed: Making Typical Activation Values Matter In Deep Learning Computing. CoRR abs/1804.06732 (2018) - [i6]Sayeh Sharify, Mostafa Mahmoud, Alberto Delmas Lascorz, Milos Nikolic, Andreas Moshovos:
Laconic Deep Learning Computing. CoRR abs/1805.04513 (2018) - 2017
- [c2]Jorge Albericio, Patrick Judd, Alberto Delmas, Sayeh Sharify, Andreas Moshovos:
Bit-Pragmatic Deep Neural Network Computing. ICLR (Workshop) 2017 - [c1]Jorge Albericio, Alberto Delmas, Patrick Judd, Sayeh Sharify, Gerard O'Leary, Roman Genov, Andreas Moshovos:
Bit-pragmatic deep neural network computing. MICRO 2017: 382-394 - [i5]Patrick Judd, Alberto Delmas Lascorz, Sayeh Sharify, Andreas Moshovos:
Cnvlutin2: Ineffectual-Activation-and-Weight-Free Deep Neural Network Computing. CoRR abs/1705.00125 (2017) - [i4]Alberto Delmas, Patrick Judd, Sayeh Sharify, Andreas Moshovos:
Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks. CoRR abs/1706.00504 (2017) - [i3]Sayeh Sharify, Alberto Delmas Lascorz, Patrick Judd, Andreas Moshovos:
Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks. CoRR abs/1706.07853 (2017) - [i2]Alberto Delmas, Sayeh Sharify, Patrick Judd, Andreas Moshovos:
Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability. CoRR abs/1707.09068 (2017) - 2016
- [i1]Jorge Albericio, Patrick Judd, Alberto Delmas Lascorz, Sayeh Sharify, Andreas Moshovos:
Bit-pragmatic Deep Neural Network Computing. CoRR abs/1610.06920 (2016)
Coauthor Index
aka: Alberto Delmas
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last updated on 2026-06-12 21:34 CEST by the dblp team
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