2 citations · 4 across the 11 of their papers we have counts for
11 papers
Brain Tumor Detection using Convolutional Neural Networks with Skip Connections
Aupam Hamran, Marzieh Vaeztourshizi, Amirhossein Esmaili +1
In this paper, we present different architectures of Convolutional Neural Networks (CNN) to analyze and classify the brain tumors into benign and malignant types using the Magnetic…
Algorithms and Hardware for Efficient Processing of Logic-based Neural Networks
Jingkai Hong, Arash Fayyazi, Amirhossein Esmaili +2
Recent efforts to improve the performance of neural network (NN) accelerators that meet today's application requirements have given rise to a new trend of logic-based NN inference…
Sparse Periodic Systolic Dataflow for Lowering Latency and Power Dissipation of Convolutional Neural Network Accelerators
Jung Hwan Heo, Arash Fayyazi, Amirhossein Esmaili +1
This paper introduces the sparse periodic systolic (SPS) dataflow, which advances the state-of-the-art hardware accelerator for supporting lightweight neural networks. Specifically…
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic
Mahdi Nazemi, Arash Fayyazi, Amirhossein Esmaili +3
While there is a large body of research on efficient processing of deep neural networks (DNNs), ultra-low-latency realization of these models for applications with stringent, sub-m…
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning
Mahdi Nazemi, Amirhossein Esmaili, Arash Fayyazi +1
Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example, neural networks (NNs…
HIPE-MAGIC: A Technology-Aware Synthesis and Mapping Flow for HIghly Parallel Execution of Memristor-Aided LoGIC
Arash Fayyazi, Amirhossein Esmaili, Massoud Pedram
Recent efforts for finding novel computing paradigms that meet today's design requirements have given rise to a new trend of processing-in-memory relying on non-volatile memories.…
Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning
Amirhossein Esmaili, Massoud Pedram
Energy consumption is one of the most critical concerns in designing computing devices, ranging from portable embedded systems to computer cluster systems. Furthermore, in the past…
Energy-Aware Scheduling of Task Graphs with Imprecise Computations and End-to-End Deadlines
Amirhossein Esmaili, Mahdi Nazemi, Massoud Pedram
Imprecise computations provide an avenue for scheduling algorithms developed for energy-constrained computing devices by trading off output quality with the utilization of system r…
BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services
Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram
Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. Thi…
Towards Collaborative Intelligence Friendly Architectures for Deep Learning
Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram
Modern mobile devices are equipped with high-performance hardware resources such as graphics processing units (GPUs), making the end-side intelligent services more feasible. Even r…
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…