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 …

Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a
wide range of cognitive tasks, and due to this, they have received significant interest from the …

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

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 …

Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …

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

This paper presents Timeloop, an infrastructure for evaluating and exploring the architecture
design space of deep neural network (DNN) accelerators. Timeloop uses a concise and …

Convolutional neural networks (CNNs) require numerous computations and external
memory accesses. Frequent accesses to off-chip memory cause slow processing and large …

Deep neural networks (DNN) have demonstrated highly promising results across computer
vision and speech recognition, and are becoming foundational for ubiquitous AI. The …

In recent years, the limits of the multicore approach emerged in the so-called “dark silicon”
issue and diminishing returns of an ever-increasing core count. Hardware manufacturers …