Hardware accelerators for deep neural networks (DNNs) have established themselves over the past decade. Most developments have worked towards higher efficiency with an individual application in mind. This highlights the strong relationship between co-designing the accelerator together with the requirements of the application. Currently for a structured design flow, however, it lacks a tool to evaluate a DNN accelerator embedded in a System on Chip (SoC) platform.To address this gap in the state of the art, we introduce FLECSim, a tool framework that enables an end-to-end simulation of an SoC with dedicated accelerators, CPUs and memories. FLECSim offers flexible configuration of the system and straightforward integration of new accelerator ...
This these presents a series of end-to-end benchmark frameworks, to evaluate the state-of-the-art co...
Neural networks have contributed significantly in applications that had been difficult to implement ...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
Recently there has been a rapidly growing demand for faster machine learning (ML) processing in data...
Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and...
Current applications that require processing of large amounts of data, such as in healthcare, trans...
Deep neural networks (DNN) are achieving state-of-the-art performance in many artificial intelligenc...
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks...
Deep Neural Networks (DNNs) are widely used in various application domains and achieve remarkable re...
Recent trends in deep convolutional neural networks (DCNNs) impose hardware accelerators as a viable...
Deep Neural Networks (DNNs) have been proven to be state-of-the-art for many applications. DNNs are ...
Ahstract-This paper presents the results of our analysis of the main problems that have to be solved...
To achieve faster design closure, there is a need to provide a design framework for the design of Re...
Edge computing devices inherently face tight resource constraints, which is especially apparent when...
The size of neural networks in deep learning techniques is increasing and varies significantly accor...
This these presents a series of end-to-end benchmark frameworks, to evaluate the state-of-the-art co...
Neural networks have contributed significantly in applications that had been difficult to implement ...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
Recently there has been a rapidly growing demand for faster machine learning (ML) processing in data...
Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and...
Current applications that require processing of large amounts of data, such as in healthcare, trans...
Deep neural networks (DNN) are achieving state-of-the-art performance in many artificial intelligenc...
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks...
Deep Neural Networks (DNNs) are widely used in various application domains and achieve remarkable re...
Recent trends in deep convolutional neural networks (DCNNs) impose hardware accelerators as a viable...
Deep Neural Networks (DNNs) have been proven to be state-of-the-art for many applications. DNNs are ...
Ahstract-This paper presents the results of our analysis of the main problems that have to be solved...
To achieve faster design closure, there is a need to provide a design framework for the design of Re...
Edge computing devices inherently face tight resource constraints, which is especially apparent when...
The size of neural networks in deep learning techniques is increasing and varies significantly accor...
This these presents a series of end-to-end benchmark frameworks, to evaluate the state-of-the-art co...
Neural networks have contributed significantly in applications that had been difficult to implement ...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...