Add to ORKGConvolutional neural networks (CNNs) are computationally intensive, and deep learning hardware should be implemented energy-efficiently for embedded systems or battery-constrained systems. In this paper, we propose an outlier-Aware time-multiplexing MAC. We exploit a CNN feature maps' characteristic of being able to express most of the data in a low bit-width except a few large values, which we call 'outliers' Our outlier-Aware time-multiplexing MAC has improved the energy efficiency by up to 21.1% compared to conventional MACs.1
Convolutional Neural Networks (CNNs) are becoming a fundamental tool for machine learning. High perf...
In recent years, deep neural networks (DNNs) have revolutionized the field of machine learning. DNNs...
Multi-FPGA platforms like Amazon Web Services F1 are perfect to accelerate multi-kernel pipelined ap...
This paper presents a convolutional neural network (CNN) accelerator that can skip zero weights and ...
Machine Learning is finding applications in a wide variety of areas ranging from autonomous cars to ...
Stochastic computing (SC) is a promising computing paradigm that can help address both the uncertain...
Owing to the presence of large values, which we call outliers, conventional methods of quantization ...
Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for ...
Deep learning such as Convolutional Neural Networks (CNNs) are an important workload increasingly de...
Applications of neural networks have gained significant importance in embedded mobile devices and In...
This paper presents a novel method to double the computation rate of convolutional neural network (C...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
Stochastic computing (SC) allows for extremely low cost and low power implementations of common arit...
There is great attention to develop hardware accelerator with better energy efficiency, as well as t...
The entangled guardbands in terms of timing specification and energy budget ensure a system against ...
Convolutional Neural Networks (CNNs) are becoming a fundamental tool for machine learning. High perf...
In recent years, deep neural networks (DNNs) have revolutionized the field of machine learning. DNNs...
Multi-FPGA platforms like Amazon Web Services F1 are perfect to accelerate multi-kernel pipelined ap...
This paper presents a convolutional neural network (CNN) accelerator that can skip zero weights and ...
Machine Learning is finding applications in a wide variety of areas ranging from autonomous cars to ...
Stochastic computing (SC) is a promising computing paradigm that can help address both the uncertain...
Owing to the presence of large values, which we call outliers, conventional methods of quantization ...
Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for ...
Deep learning such as Convolutional Neural Networks (CNNs) are an important workload increasingly de...
Applications of neural networks have gained significant importance in embedded mobile devices and In...
This paper presents a novel method to double the computation rate of convolutional neural network (C...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
Stochastic computing (SC) allows for extremely low cost and low power implementations of common arit...
There is great attention to develop hardware accelerator with better energy efficiency, as well as t...
The entangled guardbands in terms of timing specification and energy budget ensure a system against ...
Convolutional Neural Networks (CNNs) are becoming a fundamental tool for machine learning. High perf...
In recent years, deep neural networks (DNNs) have revolutionized the field of machine learning. DNNs...
Multi-FPGA platforms like Amazon Web Services F1 are perfect to accelerate multi-kernel pipelined ap...