Add to ORKGNeural networks are a subset of machine learning that are currently rapidly being deployed for various purposes, propelling the growth of science and technology. Current hardware limitations prove to be a challenge for efficient neural network implementations due to lack of several bottlenecks. Neural hardware accelerators are currently being researched and developed to aid in the training and deployment of neural networks. The objective of this final year project is to create an in-memory computing reconfigurable bitcell with variable bit weight precision to perform multiply-accumulate for use in convolutional neural networks. It is to be tested and implemented with proper input/output circuitry in the form of a macro. The design and im...
Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for ...
Convolutional neural networks have been widely employed for image recognition applications because o...
DoctorWhile Deep Neural Networks (DNNs) have shown cutting-edge performance on various applications,...
There are several possible hardware implementations of neural networks based either on digital, anal...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
Neural network mixed-mode hardware accelerators for deep convolutional neural networks (CNN) strive ...
Convolution Neural Network (CNN) is a special kind of neural network that is inspired by the behavio...
In this work, we present a novel 8T static random access memory (SRAM)-based compute-in-memory (CIM)...
The aim of this project is to develop customizable hardware that can perform Machine Learning tasks....
During the last years, Convolutional Neural Networks have been used for different applications thank...
Cellular Neural Networks (CNNs) are massively parallel nonlinear locally connected analog cells; the...
With the evolution of machine learning algorithms they are seeing a wider use in traditional signal ...
With the evolution of machine learning algorithms they are seeing a wider use in traditional signal ...
With the explosion of AI in recent years, there has been an exponential rise in the demand for compu...
With the explosion of AI in recent years, there has been an exponential rise in the demand for compu...
Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for ...
Convolutional neural networks have been widely employed for image recognition applications because o...
DoctorWhile Deep Neural Networks (DNNs) have shown cutting-edge performance on various applications,...
There are several possible hardware implementations of neural networks based either on digital, anal...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
Neural network mixed-mode hardware accelerators for deep convolutional neural networks (CNN) strive ...
Convolution Neural Network (CNN) is a special kind of neural network that is inspired by the behavio...
In this work, we present a novel 8T static random access memory (SRAM)-based compute-in-memory (CIM)...
The aim of this project is to develop customizable hardware that can perform Machine Learning tasks....
During the last years, Convolutional Neural Networks have been used for different applications thank...
Cellular Neural Networks (CNNs) are massively parallel nonlinear locally connected analog cells; the...
With the evolution of machine learning algorithms they are seeing a wider use in traditional signal ...
With the evolution of machine learning algorithms they are seeing a wider use in traditional signal ...
With the explosion of AI in recent years, there has been an exponential rise in the demand for compu...
With the explosion of AI in recent years, there has been an exponential rise in the demand for compu...
Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for ...
Convolutional neural networks have been widely employed for image recognition applications because o...
DoctorWhile Deep Neural Networks (DNNs) have shown cutting-edge performance on various applications,...