At Facebook, machine learning provides a wide range of capabilities that drive many
aspects of user experience including ranking posts, content understanding, object detection …

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 …

Systolic Arrays are one of the most popular compute substrates within Deep Learning
accelerators today, as they provide extremely high efficiency for running dense matrix …

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 …

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

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

This paper presents the computing system design in our commercial autonomous vehicles,
and provides a detailed performance, energy, and cost analyses. Drawing from our …

Internet of Things edge intelligence requires convolutional neural network (CNN) inference
to take place in the edge devices itself. ARM big. LITTLE architecture is at the heart of …

In this paper, we introduce the Tensor Streaming Processor (TSP) architecture, a functionally-
sliced microarchitecture with memory units interleaved with vector and matrix deep learning …

This paper presents a detailed overview of sparsity exploitation in deep neural network
(DNN) accelerators. Despite the algorithmic advancements which drove DNNs to become …