4 citations · 10 across the 14 of their papers we have counts for
9 papers · 1 filter
Low-Precision Mixed-Computation Models for Inference on Edge
Seyedarmin Azizi, Mahdi Nazemi, Mehdi Kamal +1
This paper presents a mixed-computation neural network processing approach for edge applications that incorporates low-precision (low-width) Posit and low-precision fixed point (Fi…
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation
Seyedarmin Azizi, Mahdi Nazemi, Arash Fayyazi +1
As the complexity and computational demands of deep learning models rise, the need for effective optimization methods for neural network designs becomes paramount. This work introd…
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…
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…