Ruolong Lian

This paper presents on overview of the LegUp design methodology and system architecture, and discusses ongoing work on profiling, hardware/software partitioning. Expand

We consider the impact of compiler optimizations on the quality of high-level synthesis (HLS)-generated FPGA hardware. Expand

A deep-learning inference accelerator is synthesized from a C-language software program parallelized with Pthreads. Expand

We consider the impact of compiler optimizations on the quality of high-level synthesis (HLS)-generated field-programmable gate array (FPGA) hardware. Expand

In this paper, we overview the LegUp framework and describe several recent developments: 1) support for an embedded ARM processor, as is available on Altera's recently released SoC FPGA, 2) HLS support for software parallelization schemes -- pthreads and OpenMP, 3) enhancements to LegUp's core HLS algorithms that raise the quality of the auto-generated hardware, and, 4) a preliminary debugging and verification framework. Expand

LegUp is a High-level Synthesis tool under active development at the University of Toronto since 2011. Expand

In recent years, FPGAs have been deployed in data centres of major cloud service providers, such as Microsoft [1], Amazon [2], Alibaba [3], Tencent [4], Huawei [5], and Nimbix [6]. Expand

We use the producer-consumer pattern, commonly used in multi-threaded programming, to infer the generation of hardware that can exploit both pipeline and spatial parallelism on FPGAs. Expand

A Framework for FPGA-Based Acceleration of Neural Network Inference with Limited Numerical Precision via High-Level Synthesis with Streaming Functionality Ruo Long Lian Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2016 Deep neural networks are achieving state-of-the-art performance in many artificial intelligence tasks, such as computer vision and speech recognition. Expand

We apply high-level synthesis (HLS) to generate Blokus Duo game-playing hardware for the FPT 2013 Design Competition. Expand