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 …

Executing machine learning workloads locally on resource constrained microcontrollers
(MCUs) promises to drastically expand the application space of IoT. However, so-called …

Nowadays, devices are equipped with advanced sensors with higher processing and
computing capabilities. Besides, widespread Internet availability enables communication …

Exploiting sparsity is a key technique in accelerating quantized convolutional neural network
(CNN) inference on mobile devices. Prior sparse CNN accelerators largely exploit …

Exploiting sparsity in deep neural networks (DNNs) has been a promising area to meet the
growing computation need of modern DNNs. However, in practice, sparse DNN acceleration …

Kronecker Matrix-Matrix Multiplication (Kron-Matmul) is the multiplication of a matrix with the
Kronecker Product of several smaller matrices. Kron-Matmul is a core operation for many …

Recent works have explored the use of weight sparsity to improve the training efficiency (test
accuracy wrt training FLOPs) of deep neural networks (DNNs). These works aim to reduce …

Linear algebra kernels are widely used in various fields such as machine learning, data
science, physical science, and graph analysis. Many of these applications work with sparse …

Pervasive intelligence promises to revolutionize society from Industrial Internet of Things
(IIoT), to smart infrastructure and homes, to personal health monitoring. Unfortunately, many …

While the use of Artificial Intelligence (AI) has faced a sharp up-rise in popularity in ground-
based industries, such as for autonomous navigation in the automotive industry and …