Deep neural networks (DNNs) have undergone a surge in popularity with consistent advances in the state of the art for tasks including image recognition, natural language processing, and speech recognition. The computationally expensive nature of these networks has led to the proliferation of implementations that sacrifice abstraction for high performance. In this paper, we present Latte, a domain-specific language for DNNs that provides a natural abstraction for specifying new layers without sacrificing performance. Users of Latte express DNNs as ensembles of neurons with connections between them. The Latte compiler synthesizes a program based on the user specification, applies a suite of domainspecific and general optimizations, and emits efficient machine code for heterogeneous architectures. Latte also includes a communication runtime for distributed memory data-parallelism. Using networks described using Latte, we demonstrate 3-6× speedup over Caffe (C++/MKL) on the three state-of-the-art ImageNet models executing on an Intel Xeon E5-2699 v3 x86 CPU.
Deep neural networks (DNNs) have undergone a surge in popularity with consistent advances in the state of the art for tasks including image recognition, natural language processing, and speech recognition. The computationally expensive nature of these networks has led to the proliferation of implementations that sacrifice abstraction for high performance. In this paper, we present Latte, a domain-specific language for DNNs that provides a natural abstraction for specifying new layers without sacrificing performance. Users of Latte express DNNs as ensembles of neurons with connections between them. The Latte compiler synthesizes a program based on the user specification, applies a suite of domainspecific and general optimizations, and emits efficient machine code for heterogeneous architectures. Latte also includes a communication runtime for distributed memory data-parallelism. Using networks described using Latte, we demonstrate 3-6× speedup over Caffe (C++/MKL) on the three state-of-the-art ImageNet models executing on an Intel Xeon E5-2699 v3 x86 CPU.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.