Massoud Pedram

Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks

The high energy cost of processing deep convolutional neural networks im...
01/29/2020 ∙ by Souvik Kundu, et al. ∙ 20 ∙ share

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Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space

Recent advances in the field of artificial intelligence have been made p...
02/12/2020 ∙ by Mohammad Saeed Abrishami, et al. ∙ 12 ∙ share

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Run-time Deep Model Multiplexing

We propose a framework to design a light-weight neural multiplexer that ...
01/14/2020 ∙ by Amir Erfan Eshratifar, et al. ∙ 6 ∙ share

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High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis

Independent Component Analysis (ICA) is a dimensionality reduction techn...
07/06/2017 ∙ by Mahdi Nazemi, et al. ∙ 0 ∙ share

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FFT-Based Deep Learning Deployment in Embedded Systems

Deep learning has delivered its powerfulness in many application domains...
12/13/2017 ∙ by Sheng Lin, et al. ∙ 0 ∙ share

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A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA

With ever-increasing application of machine learning models in various d...
01/11/2018 ∙ by Mahdi Nazemi, et al. ∙ 0 ∙ share

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Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications

Major advancements in building general-purpose and customized hardware h...
06/03/2018 ∙ by Mahdi Nazemi, et al. ∙ 0 ∙ share

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NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference

Deep neural networks have been successfully deployed in a wide variety o...
07/23/2018 ∙ by Mahdi Nazemi, et al. ∙ 0 ∙ share

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A Meta-Learning Approach for Custom Model Training

Transfer-learning and meta-learning are two effective methods to apply k...
09/21/2018 ∙ by Amir Erfan Eshratifar, et al. ∙ 0 ∙ share

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Gradient Agreement as an Optimization Objective for Meta-Learning

This paper presents a novel optimization method for maximizing generaliz...
10/18/2018 ∙ by Amir Erfan Eshratifar, et al. ∙ 0 ∙ share

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Space Expansion of Feature Selection for Designing more Accurate Error Predictors

Approximate computing is being considered as a promising design paradigm...
12/30/2018 ∙ by Shayan Tabatabaei Nikkhah, et al. ∙ 0 ∙ share

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JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services

Deep neural networks are among the most influential architectures of dee...
01/25/2018 ∙ by Amir Erfan Eshratifar, et al. ∙ 0 ∙ share

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Towards Collaborative Intelligence Friendly Architectures for Deep Learning

Modern mobile devices are equipped with high-performance hardware resour...
02/01/2019 ∙ by Amir Erfan Eshratifar, et al. ∙ 0 ∙ share

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BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services

Recent studies have shown the latency and energy consumption of deep neu...
02/04/2019 ∙ by Amir Erfan Eshratifar, et al. ∙ 0 ∙ share

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VIBNN: Hardware Acceleration of Bayesian Neural Networks

Bayesian Neural Networks (BNNs) have been proposed to address the proble...
02/02/2018 ∙ by Ruizhe Cai, et al. ∙ 0 ∙ share

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Energy-Aware Scheduling of Task Graphs with Imprecise Computations and End-to-End Deadlines

Imprecise computations provide an avenue for scheduling algorithms devel...
05/10/2019 ∙ by Amirhossein Esmaili, et al. ∙ 0 ∙ share

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Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification

Small inter-class and large intra-class variations are the main challeng...
09/06/2019 ∙ by Amir Erfan Eshratifar, et al. ∙ 0 ∙ share

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Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning

Energy consumption is one of the most critical concerns in designing com...
12/11/2019 ∙ by Amirhossein Esmaili, et al. ∙ 0 ∙ share

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NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework

The shrinking of transistor geometries as well as the increasing complex...
02/13/2020 ∙ by Mohammad Saeed Abrishami, et al. ∙ 0 ∙ share

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Hybrid Cell Assignment and Sizing for Power, Area, Delay Product Optimization of SRAM Arrays

Memory accounts for a considerable portion of the total power budget and...
02/01/2019 ∙ by Ghasem Pasandi, et al. ∙ 0 ∙ share

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Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach

Approximate Logic Synthesis (ALS) is the process of synthesizing and map...
02/01/2019 ∙ by Ghasem Pasandi, et al. ∙ 0 ∙ share

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Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework

Machine learning applied to architecture design presents a promising opp...
05/11/2019 ∙ by Ting-Ru Lin, et al. ∙ 0 ∙ share

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Modeling Processor Idle Times in MPSoC Platforms to Enable Integrated DPM, DVFS, and Task Scheduling Subject to a Hard Deadline

Energy efficiency is one of the most critical design criteria for modern...
12/19/2018 ∙ by Amirhossein Esmaili, et al. ∙ 0 ∙ share

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CSM-NN: Current Source Model Based Logic Circuit Simulation – A Neural Network Approach

The miniaturization of transistors down to 5nm and beyond, plus the incr...
02/13/2020 ∙ by Mohammad Saeed Abrishami, et al. ∙ 0 ∙ share

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qBSA: Logic Design of a 32-bit Block-Skewed RSFQ Arithmetic Logic Unit

Single flux quantum (SFQ) circuits are an attractive beyond-CMOS technol...
01/29/2020 ∙ by Souvik Kundu, et al. ∙ 0 ∙ share

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Massoud Pedram

Featured Co-authors

Yanzhi Wang

Caiwen Ding

Ning Liu

Xuehai Qian

Sheng Lin

Peter A. Beerel

David Eigen

Shahin Nazarian

Keith M. Chugg

Ao Ren

Amir Erfan Eshratifar

Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks

Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space

Run-time Deep Model Multiplexing

High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis

FFT-Based Deep Learning Deployment in Embedded Systems

A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA

Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications

NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference

A Meta-Learning Approach for Custom Model Training

Gradient Agreement as an Optimization Objective for Meta-Learning

Space Expansion of Feature Selection for Designing more Accurate Error Predictors

JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services

Towards Collaborative Intelligence Friendly Architectures for Deep Learning

BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services

VIBNN: Hardware Acceleration of Bayesian Neural Networks

Energy-Aware Scheduling of Task Graphs with Imprecise Computations and End-to-End Deadlines

Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification

Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning

NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework

Hybrid Cell Assignment and Sizing for Power, Area, Delay Product Optimization of SRAM Arrays

Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach

Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework

Modeling Processor Idle Times in MPSoC Platforms to Enable Integrated DPM, DVFS, and Task Scheduling Subject to a Hard Deadline

CSM-NN: Current Source Model Based Logic Circuit Simulation – A Neural Network Approach

qBSA: Logic Design of a 32-bit Block-Skewed RSFQ Arithmetic Logic Unit