arXiv subjects

Tian Zhao

Publications and source records attributed to Tian Zhao.

21 records · Page 2Linked to original sources

arXiv2019

Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator

Recurrent Neural Network (RNN) applications form a major class of AI-powered, low-latency data center workloads. Most execution models for RNN acceleration break computation graphs into BLAS kernels, which lead to significant inter-kernel data movement and resource underutilization. We show that by supporting more general loop constructs that capture design parameters in accelerators, it is possible to improve resource utilization using cross-kernel optimization without sacrificing programmability. Such abstraction level enables a design space search that can lead to efficient usage of on-chip resources on a spatial architecture across a range of problem sizes. We evaluate our optimization strategy on such abstraction with DeepBench using a configurable spatial accelerator. We demonstrate that this implementation provides a geometric speedup of 30x in performance, 1.6x in area, and 2x in power efficiency compared to a Tesla V100 GPU, and a geometric speedup of 2x compared to Microsoft Brainwave implementation on a Stratix 10 FPGA.

cs.DC↗

arXiv2019

Epitaxial Graphene Intercalation: A Route to Graphene Modulation and Unique 2D Materials

Intercalation of atomic species through epitaxial graphene layers began only a few years following its initial report in 2004. The impact of intercalation on the electronic properties of the graphene is well known; however, the intercalant itself can also exhibit intriguing properties not found in nature. This suggests that a shift in the focus of epitaxial graphene intercalation studies may lead to fruitful exploration of many new forms of traditionally 3D materials. In the following forward-looking review, we summarize the primary techniques used to achieve and characterize EG intercalation, and introduce a new, facile approach to readily achieve metal intercalation at the graphene/silicon carbide interface. We show that simple thermal evaporation-based methods can effectively replace complicated synthesis techniques to realize large-scale intercalation of non-refractory metals. We also show that these methods can be extended to the formation of compound materials based on intercalation. Two-dimensional (2D) silver (2D-Ag) and large-scale 2D gallium nitride (2D-GaNx) are used to demonstrate these approaches.

cond-mat.mtrl-sci↗

arXiv2017

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

In recent years, Deep Learning (DL) has found great success in domains such as multimedia understanding. However, the complex nature of multimedia data makes it difficult to develop DL-based software. The state-of-the art tools, such as Caffe, TensorFlow, Torch7, and CNTK, while are successful in their applicable domains, are programming libraries with fixed user interface, internal representation, and execution environment. This makes it difficult to implement portable and customized DL applications. In this paper, we present DeepDSL, a domain specific language (DSL) embedded in Scala, that compiles deep networks written in DeepDSL to Java source code. Deep DSL provides (1) intuitive constructs to support compact encoding of deep networks; (2) symbolic gradient derivation of the networks; (3) static analysis for memory consumption and error detection; and (4) DSL-level optimization to improve memory and runtime efficiency. DeepDSL programs are compiled into compact, efficient, customizable, and portable Java source code, which operates the CUDA and CUDNN interfaces running on Nvidia GPU via a Java Native Interface (JNI) library. We evaluated DeepDSL with a number of popular DL networks. Our experiments show that the compiled programs have very competitive runtime performance and memory efficiency compared to the existing libraries.

cs.PL↗