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2020 – today
- 2024
[j5]Seyedarmin Azizi
, Mahdi Nazemi, Mehdi Kamal, Massoud Pedram:
Low-Precision Mixed-Computation Models for Inference on Edge. IEEE Trans. Very Large Scale Integr. Syst. 32(8): 1414-1422 (2024)- [c15]Seyedarmin Azizi, Mahdi Nazemi, Arash Fayyazi, Massoud Pedram:
Automated Optimization of Deep Neural Networks: Dynamic Bit-Width and Layer-Width Selection via Cluster-Based Parzen Estimation. DATE 2024: 1-6 - [c14]Arash Fayyazi, Mahdi Nazemi, Arya Fayyazi, Massoud Pedram:
NeuroBlend: Towards Low-Power yet Accurate Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions. ACM Great Lakes Symposium on VLSI 2024: 730-735 - [i19]Seyedarmin Azizi, Mahdi Nazemi, Massoud Pedram:
Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy. CoRR abs/2402.06004 (2024) - 2023
- [j4]Soheil Nazar Shahsavani, Arash Fayyazi, Mahdi Nazemi, Massoud Pedram:
Efficient Compilation and Mapping of Fixed Function Combinational Logic onto Digital Signal Processors Targeting Neural Network Inference and Utilizing High-level Synthesis. ACM Trans. Reconfigurable Technol. Syst. 16(2): 17:1-17:25 (2023) - [c13]Jingkai Hong, Arash Fayyazi, Amirhossein Esmaili, Mahdi Nazemi, Massoud Pedram:
Algorithms and Hardware for Efficient Processing of Logic-based Neural Networks. DAC 2023: 1-6 - [i18]Jung Hwan Heo, Arash Fayyazi, Mahdi Nazemi, Massoud Pedram:
A Fast Training-Free Compression Framework for Vision Transformers. CoRR abs/2303.02331 (2023) - [i17]Jingkai Hong, Arash Fayyazi, Amirhossein Esmaili, Mahdi Nazemi, Massoud Pedram:
Algorithms and Hardware for Efficient Processing of Logic-based Neural Networks. CoRR abs/2304.06299 (2023) - [i16]Jung Hwan Heo, Seyedarmin Azizi, Arash Fayyazi, Mahdi Nazemi, Massoud Pedram:
SNT: Sharpness-Minimizing Network Transformation for Fast Compression-friendly Pretraining. CoRR abs/2305.04526 (2023) - [i15]Arash Fayyazi, Mahdi Nazemi, Armin Abdollahi, Massoud Pedram:
BlendNet: Design and Optimization of a Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions. CoRR abs/2307.03784 (2023) - [i14]Seyedarmin Azizi, Mahdi Nazemi, Arash Fayyazi, Massoud Pedram:
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation. CoRR abs/2308.06422 (2023) - [i13]Seyedarmin Azizi, Mahdi Nazemi, Mehdi Kamal, Massoud Pedram:
Low-Precision Mixed-Computation Models for Inference on Edge. CoRR abs/2312.02210 (2023) - 2022
- [i12]Soheil Nazar Shahsavani, Arash Fayyazi, Mahdi Nazemi, Massoud Pedram:
Efficient Compilation and Mapping of Fixed Function Combinational Logic onto Digital Signal Processors Targeting Neural Network Inference and Utilizing High-level Synthesis. CoRR abs/2208.00302 (2022) - 2021
- [j3]Mohammad Javad Dousti, Qing Xie, Mahdi Nazemi, Massoud Pedram:
Therminator 2: A Fast Thermal Simulator for Portable Devices. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 40(12): 2528-2541 (2021) - [c12]Souvik Kundu, Mahdi Nazemi, Peter A. Beerel, Massoud Pedram:
DNR: A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs. ASP-DAC 2021: 344-350 - [c11]Hitarth Kanakia, Mahdi Nazemi, Arash Fayyazi, Massoud Pedram:
ESPRESSO-GPU: Blazingly Fast Two-Level Logic Minimization. DATE 2021: 1038-1043 - [c10]Mahdi Nazemi, Arash Fayyazi, Amirhossein Esmaili, Atharva Khare, Soheil Nazar Shahsavani, Massoud Pedram:
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic. FCCM 2021: 266-267 - [c9]Mahdi Nazemi, Hitarth Kanakia, Massoud Pedram:
Heuristics for Million-scale Two-level Logic Minimization. ICCAD 2021: 1-7 - [i11]Mahdi Nazemi, Arash Fayyazi, Amirhossein Esmaili, Atharva Khare, Soheil Nazar Shahsavani, Massoud Pedram:
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic. CoRR abs/2104.05421 (2021) - 2020
- [j2]Souvik Kundu, Mahdi Nazemi, Massoud Pedram, Keith M. Chugg, Peter A. Beerel:
Pre-Defined Sparsity for Low-Complexity Convolutional Neural Networks. IEEE Trans. Computers 69(7): 1045-1058 (2020) - [j1]Amirhossein Esmaili, Mahdi Nazemi, Massoud Pedram:
Energy-aware Scheduling of Task Graphs with Imprecise Computations and End-to-end Deadlines. ACM Trans. Design Autom. Electr. Syst. 25(1): 11:1-11:21 (2020) - [c8]Mahdi Nazemi, Amirhossein Esmaili, Arash Fayyazi, Massoud Pedram:
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning. ICCAD 2020: 89:1-89:9 - [i10]Souvik Kundu, Mahdi Nazemi, Massoud Pedram, Keith M. Chugg, Peter A. Beerel:
Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks. CoRR abs/2001.10710 (2020) - [i9]Mahdi Nazemi, Amirhossein Esmaili, Arash Fayyazi, Massoud Pedram:
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning. CoRR abs/2007.15222 (2020) - [i8]Souvik Kundu, Mahdi Nazemi, Peter A. Beerel, Massoud Pedram:
A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs. CoRR abs/2011.03083 (2020)
2010 – 2019
- 2019
- [c7]Mahdi Nazemi, Ghasem Pasandi, Massoud Pedram:
Energy-efficient, low-latency realization of neural networks through boolean logic minimization. ASP-DAC 2019: 274-279 - [c6]Amirhossein Esmaili, Mahdi Nazemi, Massoud Pedram:
Modeling processor idle times in MPSoC platforms to enable integrated DPM, DVFS, and task scheduling subject to a hard deadline. ASP-DAC 2019: 532-537 - [i7]Amirhossein Esmaili, Mahdi Nazemi, Massoud Pedram:
Energy-Aware Scheduling of Task Graphs with Imprecise Computations and End-to-End Deadlines. CoRR abs/1905.04391 (2019) - 2018
- [c5]Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, Massoud Pedram:
FFT-based deep learning deployment in embedded systems. DATE 2018: 1045-1050 - [c4]Mahdi Nazemi, Massoud Pedram:
Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications. ISLPED 2018: 48:1-48:6 - [c3]Mahdi Nazemi, Amir Erfan Eshratifar, Massoud Pedram:
A hardware-friendly algorithm for scalable training and deployment of dimensionality reduction models on FPGA. ISQED 2018: 395-400 - [i6]Mahdi Nazemi, Amir Erfan Eshratifar, Massoud Pedram:
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA. CoRR abs/1801.04014 (2018) - [i5]Mahdi Nazemi, Massoud Pedram:
Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications. CoRR abs/1806.00875 (2018) - [i4]Mahdi Nazemi, Ghasem Pasandi, Massoud Pedram:
NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference. CoRR abs/1807.08716 (2018) - [i3]Amirhossein Esmaili, Mahdi Nazemi, Massoud Pedram:
Modeling Processor Idle Times in MPSoC Platforms to Enable Integrated DPM, DVFS, and Task Scheduling Subject to a Hard Deadline. CoRR abs/1812.07723 (2018) - 2017
- [c2]Mahdi Nazemi, Shahin Nazarian, Massoud Pedram:
High-performance FPGA implementation of equivariant adaptive separation via independence algorithm for Independent Component Analysis. ASAP 2017: 25-28 - [i2]Mahdi Nazemi, Shahin Nazarian, Massoud Pedram:
High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis. CoRR abs/1707.01939 (2017) - [i1]Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, Massoud Pedram:
FFT-Based Deep Learning Deployment in Embedded Systems. CoRR abs/1712.04910 (2017) - 2015
- [c1]Mohammad Javad Dousti, Majid Ghasemi-Gol, Mahdi Nazemi, Massoud Pedram:
ThermTap: An online power analyzer and thermal simulator for Android devices. ISLPED 2015: 341-346
Coauthor Index
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last updated on 2024-10-01 20:48 CEST by the dblp team
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