Machine learning for microcontroller-class hardware: A review
The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …
FPGA-based accelerators of deep learning networks for learning and classification: A review
Due to recent advances in digital technologies, and availability of credible data, an area of
artificial intelligence, deep learning, has emerged and has demonstrated its ability and …
artificial intelligence, deep learning, has emerged and has demonstrated its ability and …
{TVM}: An automated {End-to-End} optimizing compiler for deep learning
There is an increasing need to bring machine learning to a wide diversity of hardware
devices. Current frameworks rely on vendor-specific operator libraries and optimize for a …
devices. Current frameworks rely on vendor-specific operator libraries and optimize for a …
Randomized numerical linear algebra: Foundations and algorithms
This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …
Morpho and Rvcg–shape analysis in R: R-packages for geometric morphometrics, shape analysis and surface manipulations
The mathematical/statistical software platform R has seen an immense increase in
popularity within the last decade. Its main advantages are its flexibility, a large repository of …
popularity within the last decade. Its main advantages are its flexibility, a large repository of …
Learning to optimize tensor programs
We introduce a learning-based framework to optimize tensor programs for deep learning
workloads. Efficient implementations of tensor operators, such as matrix multiplication and …
workloads. Efficient implementations of tensor operators, such as matrix multiplication and …
Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions
Deep learning models with convolutional and recurrent networks are now ubiquitous and
analyze massive amounts of audio, image, video, text and graph data, with applications in …
analyze massive amounts of audio, image, video, text and graph data, with applications in …
Ansor: Generating {High-Performance} tensor programs for deep learning
High-performance tensor programs are crucial to guarantee efficient execution of deep
neural networks. However, obtaining performant tensor programs for different operators on …
neural networks. However, obtaining performant tensor programs for different operators on …
Throughput-optimized OpenCL-based FPGA accelerator for large-scale convolutional neural networks
Convolutional Neural Networks (CNNs) have gained popularity in many computer vision
applications such as image classification, face detection, and video analysis, because of …
applications such as image classification, face detection, and video analysis, because of …
OpenMx 2.0: Extended structural equation and statistical modeling
The new software package OpenMx 2.0 for structural equation and other statistical modeling
is introduced and its features are described. OpenMx is evolving in a modular direction and …
is introduced and its features are described. OpenMx is evolving in a modular direction and …