BACKGROUND Improvements in computing power can claim a large share of the credit for
many of the things that we take for granted in our modern lives: cellphones that are more …

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 …

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 …

Generalized tensor algebra is a prime candidate for acceleration via customized ASICs.
Modern tensors feature a wide range of data sparsity, with the density of non-zero elements …

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 …

Meta has traditionally relied on using CPU-based servers for running inference workloads,
specifically Deep Learning Recommendation Models (DLRM), but the increasing compute …

Domain-specific hardware accelerators can provide orders of magnitude speedup and
energy efficiency over general purpose processors. However, they require extensive manual …

Deep neural networks have been adopted in a wide range of application domains, leading
to high demand for inference accelerators. However, the high cost associated with ASIC …

The design of specialized architectures for accelerating the inference procedure of Deep
Neural Networks (DNNs) is a booming area of research nowadays. While first-generation …

The energy efficiency of deep neural network (DNN) inference can be improved with custom
accelerators. DNN inference accelerators often employ specialized hardware techniques to …