82 citations · 321 across the 33 of their papers we have counts for
11 papers · 1 filter
Space Expansion of Feature Selection for Designing more Accurate Error Predictors
Shayan Tabatabaei Nikkhah, Mehdi Kamal, Ali Afzali-Kusha +1
Approximate computing is being considered as a promising design paradigm to overcome the energy and performance challenges in computationally demanding applications. If the case wh…
Modeling Processor Idle Times in MPSoC Platforms to Enable Integrated DPM, DVFS, and Task Scheduling Subject to a Hard Deadline
Amirhossein Esmaili, Mahdi Nazemi, Massoud Pedram
Energy efficiency is one of the most critical design criteria for modern embedded systems such as multiprocessor system-on-chips (MPSoCs). Dynamic voltage and frequency scaling (DV…
Gradient Agreement as an Optimization Objective for Meta-Learning
Amir Erfan Eshratifar, David Eigen, Massoud Pedram
This paper presents a novel optimization method for maximizing generalization over tasks in meta-learning. The goal of meta-learning is to learn a model for an agent adapting rapid…
A Graph Partitioning Algorithm with Application in Synthesizing Single Flux Quantum Logic Circuits
Ghasem Pasandi, Massoud Pedram
In this paper, a new graph partitioning problem is introduced. The depth of each part is constrained, i.e., the node count in the longest path of the corresponding sub-graph is no…
SpRRAM: A Predefined Sparsity Based Memristive Neuromorphic Circuit for Low Power Application
Arash Fayyazi, Souvik Kundu, Shahin Nazarian +2
In this paper, we propose an efficient predefined structured sparsity-based ex-situ training framework for a hybrid CMOS-memristive neuromorphic hardware for deep neural network to…
A Meta-Learning Approach for Custom Model Training
Amir Erfan Eshratifar, Mohammad Saeed Abrishami, David Eigen +1
Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e. whe…
NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference
Mahdi Nazemi, Ghasem Pasandi, Massoud Pedram
Deep neural networks have been successfully deployed in a wide variety of applications including computer vision and speech recognition. However, computational and storage complexi…
Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications
Mahdi Nazemi, Massoud Pedram
Major advancements in building general-purpose and customized hardware have been one of the key enablers of versatility and pervasiveness of machine learning models such as deep ne…
VIBNN: Hardware Acceleration of Bayesian Neural Networks
Ruizhe Cai, Ao Ren, Ning Liu +5
Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probabi…
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA
Mahdi Nazemi, Amir Erfan Eshratifar, Massoud Pedram
With ever-increasing application of machine learning models in various domains such as image classification, speech recognition and synthesis, and health care, designing efficient…
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
Amir Erfan Eshratifar, Mohammad Saeed Abrishami, Massoud Pedram
Deep learning models are being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants, autonomous cars, and smart home service…