arXiv subjects

David Han

Publications and source records attributed to David Han.

21 records · Page 2Linked to original sources

arXiv2021

A Statistician Teaches Deep Learning

Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the ideas and perspectives can seem alien to statisticians. Nonetheless, it is important that statisticians become involved -- many of our students need this expertise for their careers. In this paper, developed as part of a program on DL held at the Statistical and Applied Mathematical Sciences Institute, we address this culture gap and provide tips on how to teach deep learning to statistics graduate students. After some background, we list ways in which DL and statistical perspectives differ, provide a recommended syllabus that evolved from teaching two iterations of a DL graduate course, offer examples of suggested homework assignments, give an annotated list of teaching resources, and discuss DL in the context of two research areas.

stat.ML↗

arXiv2018

DLA: Compiler and FPGA Overlay for Neural Network Inference Acceleration

Overlays have shown significant promise for field-programmable gate-arrays (FPGAs) as they allow for fast development cycles and remove many of the challenges of the traditional FPGA hardware design flow. However, this often comes with a significant performance burden resulting in very little adoption of overlays for practical applications. In this paper, we tailor an overlay to a specific application domain, and we show how we maintain its full programmability without paying for the performance overhead traditionally associated with overlays. Specifically, we introduce an overlay targeted for deep neural network inference with only ~1% overhead to support the control and reprogramming logic using a lightweight very-long instruction word (VLIW) network. Additionally, we implement a sophisticated domain specific graph compiler that compiles deep learning languages such as Caffe or Tensorflow to easily target our overlay. We show how our graph compiler performs architecture-driven software optimizations to significantly boost performance of both convolutional and recurrent neural networks (CNNs/RNNs) - we demonstrate a 3x improvement on ResNet-101 and a 12x improvement for long short-term memory (LSTM) cells, compared to naive implementations. Finally, we describe how we can tailor our hardware overlay, and use our graph compiler to achieve ~900 fps on GoogLeNet on an Intel Arria 10 1150 - the fastest ever reported on comparable FPGAs.

cs.DC↗

arXiv2016

Non-negative matrix factorization-based subband decomposition for acoustic source localization

A novel non-negative matrix factorization (NMF) based subband decomposition in frequency spatial domain for acoustic source localization using a microphone array is introduced. The proposed method decomposes source and noise subband and emphasises source dominant frequency bins for more accurate source representation. By employing NMF, delay basis vectors and their subband information in frequency spatial domain for each frame is extracted. The proposed algorithm is evaluated in both simulated noise and real noise with a speech corpus database. Experimental results clearly indicate that the algorithm performs more accurately than other conventional algorithms under both reverberant and noisy acoustic environments.

cs.SD↗