Convolutional neural networks (CNNs) have proven to be a disruptive technology in most
vision, speech and image processing tasks. Given their ubiquitous acceptance, the research …

Deep Neural Networks (DNNs) have shown significant advantages in many domains, such
as pattern recognition, prediction, and control optimization. The edge computing demand in …

The advent of Deep Learning (DL) has radically transformed the computing industry across
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …

Package-level integration using multi-chip-modules (MCMs) is a promising approach for
building large-scale systems. Compared to a large monolithic die, an MCM combines many …

Existing deep learning systems commonly parallelize deep neural network (DNN) training
using data or model parallelism, but these strategies often result in suboptimal …

This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

We show that DNN accelerator micro-architectures and their program mappings represent
specific choices of loop order and hardware parallelism for computing the seven nested …

Recent advances in Deep Neural Networks (DNNs) have led to active development of
specialized DNN accelerators, many of which feature a large number of processing …

DNN layers are multi-dimensional loops that can be ordered, tiled, and scheduled in myriad
ways across space and time on DNN accelerators. Each of these choices is called a …

The compute demand for deep learning workloads is well known and is a prime motivator for
powerful parallel computing platforms such as GPUs or dedicated hardware accelerators …