Massoud Pedram

research

∙ 09/06/2023

A Josephson Parametric Oscillator-Based Ising Machine

Ising machines have emerged as a promising solution for rapidly solving ...

0 Sasan Razmkhah, et al. ∙

research

∙ 08/12/2023

Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation

As the complexity and computational demands of deep learning models rise...

0 Seyedarmin Azizi, et al. ∙

research

∙ 07/14/2023

Brain Tumor Detection using Convolutional Neural Networks with Skip Connections

In this paper, we present different architectures of Convolutional Neura...

0 Aupam Hamran, et al. ∙

research

∙ 07/07/2023

BlendNet: Design and Optimization of a Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions

This paper presents BlendNet, a neural network architecture employing a ...

0 Arash Fayyazi, et al. ∙

research

∙ 05/08/2023

SNT: Sharpness-Minimizing Network Transformation for Fast Compression-friendly Pretraining

Model compression has become the de-facto approach for optimizing the ef...

0 Jung Hwan Heo, et al. ∙

research

∙ 04/13/2023

Algorithms and Hardware for Efficient Processing of Logic-based Neural Networks

Recent efforts to improve the performance of neural network (NN) acceler...

0 Jingkai Hong, et al. ∙

research

∙ 04/11/2023

TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation

Secure computation is of critical importance to not only the DoD, but ac...

0 David Bruce Cousins, et al. ∙

research

∙ 03/30/2023

RPU: The Ring Processing Unit

Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many i...

0 Deepraj Soni, et al. ∙

research

∙ 03/04/2023

A Fast Training-Free Compression Framework for Vision Transformers

Token pruning has emerged as an effective solution to speed up the infer...

0 Jung Hwan Heo, et al. ∙

research

∙ 08/29/2022

AMR-MUL: An Approximate Maximally Redundant Signed Digit Multiplier

In this paper, we present an energy-efficient, yet high-speed approximat...

0 Saba Amanollahi, et al. ∙

research

∙ 08/17/2022

Better Than Worst-Case Decoding for Quantum Error Correction

The overheads of classical decoding for quantum error correction on supe...

0 Gokul Subramanian Ravi, et al. ∙

research

∙ 07/30/2022

Efficient Compilation and Mapping of Fixed Function Combinational Logic onto Digital Signal Processors Targeting Neural Network Inference and Utilizing High-level Synthesis

Recent efforts for improving the performance of neural network (NN) acce...

0 Soheil Nazar Shahsavani, et al. ∙

research

∙ 06/30/2022

Sparse Periodic Systolic Dataflow for Lowering Latency and Power Dissipation of Convolutional Neural Network Accelerators

This paper introduces the sparse periodic systolic (SPS) dataflow, which...

0 Jung Hwan Heo, et al. ∙

research

∙ 04/07/2021

NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic

While there is a large body of research on efficient processing of deep ...

0 Mahdi Nazemi, et al. ∙

research

∙ 01/24/2021

A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks

In this work, to limit the number of required attention inference hops i...

0 Mohsen Ahmadzadeh, et al. ∙

research

∙ 01/07/2021

BRDS: An FPGA-based LSTM Accelerator with Row-Balanced Dual-Ratio Sparsification

In this paper, first, a hardware-friendly pruning algorithm for reducing...

0 Seyed Abolfazl Ghasemzadeh, et al. ∙

research

∙ 11/03/2020

A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs

This paper presents a dynamic network rewiring (DNR) method to generate ...

0 Souvik Kundu, et al. ∙

research

∙ 07/30/2020

SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning

Machine learning models differ in terms of accuracy, computational/memor...

0 Mahdi Nazemi, et al. ∙

research

∙ 07/03/2020

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

This paper aims at integrating three powerful techniques namely Deep Lea...

0 Ghasem Pasandi, et al. ∙

research

∙ 02/13/2020

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

The shrinking of transistor geometries as well as the increasing complex...

0 Mohammad Saeed Abrishami, et al. ∙

research

∙ 02/13/2020

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...

0 Mohammad Saeed Abrishami, et al. ∙

research

∙ 02/12/2020

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

Recent advances in the field of artificial intelligence have been made p...

12 Mohammad Saeed Abrishami, et al. ∙

research

∙ 01/29/2020

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

Single flux quantum (SFQ) circuits are an attractive beyond-CMOS technol...

0 Souvik Kundu, et al. ∙

research

∙ 01/29/2020

Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks

The high energy cost of processing deep convolutional neural networks im...

20 Souvik Kundu, et al. ∙

research

∙ 01/14/2020

Run-time Deep Model Multiplexing

We propose a framework to design a light-weight neural multiplexer that ...

6 Amir Erfan Eshratifar, et al. ∙

research

∙ 12/11/2019

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...

0 Amirhossein Esmaili, et al. ∙

research

∙ 09/06/2019

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

Small inter-class and large intra-class variations are the main challeng...

0 Amir Erfan Eshratifar, et al. ∙

research

∙ 05/11/2019

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

Machine learning applied to architecture design presents a promising opp...

0 Ting-Ru Lin, et al. ∙

research

∙ 05/10/2019

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

Imprecise computations provide an avenue for scheduling algorithms devel...

0 Amirhossein Esmaili, et al. ∙

research

∙ 02/04/2019

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

Recent studies have shown the latency and energy consumption of deep neu...

0 Amir Erfan Eshratifar, et al. ∙

research

∙ 02/01/2019

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...

0 Ghasem Pasandi, et al. ∙

research

∙ 02/01/2019

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

Approximate Logic Synthesis (ALS) is the process of synthesizing and map...

0 Ghasem Pasandi, et al. ∙

research

∙ 02/01/2019

Towards Collaborative Intelligence Friendly Architectures for Deep Learning

Modern mobile devices are equipped with high-performance hardware resour...

0 Amir Erfan Eshratifar, et al. ∙

research

∙ 12/30/2018

Space Expansion of Feature Selection for Designing more Accurate Error Predictors

Approximate computing is being considered as a promising design paradigm...

0 Shayan Tabatabaei Nikkhah, et al. ∙

research

∙ 12/19/2018

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...

0 Amirhossein Esmaili, et al. ∙

research

∙ 10/18/2018

Gradient Agreement as an Optimization Objective for Meta-Learning

This paper presents a novel optimization method for maximizing generaliz...

0 Amir Erfan Eshratifar, et al. ∙

research

∙ 09/21/2018

A Meta-Learning Approach for Custom Model Training

Transfer-learning and meta-learning are two effective methods to apply k...

0 Amir Erfan Eshratifar, et al. ∙

research

∙ 07/23/2018

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

Deep neural networks have been successfully deployed in a wide variety o...

0 Mahdi Nazemi, et al. ∙

research

∙ 06/03/2018

Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications

Major advancements in building general-purpose and customized hardware h...

0 Mahdi Nazemi, et al. ∙

research

∙ 02/02/2018

VIBNN: Hardware Acceleration of Bayesian Neural Networks

Bayesian Neural Networks (BNNs) have been proposed to address the proble...

0 Ruizhe Cai, et al. ∙

research

∙ 01/25/2018

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

Deep neural networks are among the most influential architectures of dee...

0 Amir Erfan Eshratifar, et al. ∙

research

∙ 01/11/2018

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...

0 Mahdi Nazemi, et al. ∙

research

∙ 12/13/2017

FFT-Based Deep Learning Deployment in Embedded Systems

Deep learning has delivered its powerfulness in many application domains...

0 Sheng Lin, et al. ∙

research

∙ 07/06/2017

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

Independent Component Analysis (ICA) is a dimensionality reduction techn...

0 Mahdi Nazemi, et al. ∙

Massoud Pedram

Featured Co-authors