Advances in multicore processors and accelerators have opened the flood gates to greater
exploration and application of machine learning techniques to a variety of applications …

The difficulty of deploying various deep learning (DL) models on diverse DL hardware has
boosted the research and development of DL compilers in the community. Several DL …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …

Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient
devices and systems are extremely valuable across a broad range of application domains …

We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …

Although the quest for more accurate solutions is pushing deep learning research towards
larger and more complex algorithms, edge devices demand efficient inference and therefore …

Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …

We introduce an automated tool for deploying ultra low-latency, low-power deep neural
networks with convolutional layers on field-programmable gate arrays (FPGAs). By …