Add to ORKGInternational audienceDeep Neural Networks (DNN) represent a performance-hungry application. Floating-Point (FP) and custom floating-point-like arithmetic satisfies this hunger. While there is need for speed, inference in DNNs does not seem to have any need for precision. Many papers experimentally observe that DNNs can successfully run at almost ridiculously low precision. The aim of this paper is twofold: first, to shed some theoretical light upon why a DNN's FP accuracy stays high for low FP precision. We observe that the loss of relative accuracy in the convolutional steps is recovered by the activation layers, which are extremely well-conditioned. We give an interpretation for the link between precision and accuracy in DNNs. Second, th...
Due to limited size, cost and power, embedded devices do not offer the same computational throughput...
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) in...
DNNs have been finding a growing number of applications including image classification, speech recog...
The acclaimed successes of neural networks often overshadow their tremendous complexity. We focus on...
Approximate computing has emerged as a promising approach to energy-efficient design of digital syst...
Understanding the bit-width precision is critical in compact representation of a Deep Neural Network...
Recently, deep learning is at the forefront of the state-of-the-art machine learning algorithms and ...
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (D...
Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successf...
We explore unique considerations involved in fitting machine learning (ML) models to data with very ...
Recent successes of deep learning have been achieved at the expense of a very high computational and...
International audienceGraphics Processing Units (GPUs) offer the possibility to execute floating-poi...
Deep Neural Networks (DNN) are typically tested for accuracy relying on a set of unlabelled real wor...
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (D...
We present any-precision deep neural networks (DNNs), which are trained with a new method that allow...
Due to limited size, cost and power, embedded devices do not offer the same computational throughput...
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) in...
DNNs have been finding a growing number of applications including image classification, speech recog...
The acclaimed successes of neural networks often overshadow their tremendous complexity. We focus on...
Approximate computing has emerged as a promising approach to energy-efficient design of digital syst...
Understanding the bit-width precision is critical in compact representation of a Deep Neural Network...
Recently, deep learning is at the forefront of the state-of-the-art machine learning algorithms and ...
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (D...
Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successf...
We explore unique considerations involved in fitting machine learning (ML) models to data with very ...
Recent successes of deep learning have been achieved at the expense of a very high computational and...
International audienceGraphics Processing Units (GPUs) offer the possibility to execute floating-poi...
Deep Neural Networks (DNN) are typically tested for accuracy relying on a set of unlabelled real wor...
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (D...
We present any-precision deep neural networks (DNNs), which are trained with a new method that allow...
Due to limited size, cost and power, embedded devices do not offer the same computational throughput...
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) in...
DNNs have been finding a growing number of applications including image classification, speech recog...