Contents Modern workloads, such as deep neural networks (DNNs), increasingly rely on dense arithmetic compute patterns that are ill-suited for general-purpose processors, leading to a rise in domain-specific compute accelerators [1]. Many of these workloads can benefit from varying precision during computation, e.g. different precisions among layers and between training and inference for DNNs has been shown to improve energy efficiency [2].
Abstract:
Modern workloads, such as deep neural networks (DNNs), increasingly rely on dense arithmetic compute patterns that are ill-suited for general-purpose processors, leading ...Show MoreMetadata
Abstract:
Modern workloads, such as deep neural networks (DNNs), increasingly rely on dense arithmetic compute patterns that are ill-suited for general-purpose processors, leading to a rise in domain-specific compute accelerators [1]. Many of these workloads can benefit from varying precision during computation, e.g. different precisions among layers and between training and inference for DNNs has been shown to improve energy efficiency [2].
Date of Conference: 13-22 February 2021
Date Added to IEEE Xplore: 03 March 2021
ISBN Information:
