Massoud Pedram

Deep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis

This paper aims at integrating three powerful techniques namely Deep Lea...
07/03/2020 ∙ by Ghasem Pasandi, 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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

Deep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis

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

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

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

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

Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks

Run-time Deep Model Multiplexing

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

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

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

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

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

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

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

Towards Collaborative Intelligence Friendly Architectures for Deep Learning

Space Expansion of Feature Selection for Designing more Accurate Error Predictors

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

Gradient Agreement as an Optimization Objective for Meta-Learning

A Meta-Learning Approach for Custom Model Training

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

Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications

VIBNN: Hardware Acceleration of Bayesian Neural Networks

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

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

FFT-Based Deep Learning Deployment in Embedded Systems

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