FPGA Acceleration for Computational Glass-Free Displays

Zhuolun He, Guojie Luo
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Abstract

The increasing computational power enables various new applications that are runtime prohibitive before. FPGA is one of such computational power with both reconfigurability and energy efficiency. In this paper, we demonstrate the feasibility of eyeglasses-free displays through FPGA acceleration. Specifically, we propose several techniques to accelerate the sparse matrix-vector multiplication and the L-BFGS iterative optimization algorithm with the consideration of the characteristics of FPGAs. The experimental results show that we reach a $12.78X$ overall speedup of the glass-free display application.
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用于计算无玻璃显示器的FPGA加速
不断增强的计算能力使以前运行时禁止的各种新应用程序成为可能。FPGA就是这样一种具有可重构性和能效的计算能力。在本文中,我们论证了通过FPGA加速实现无眼镜显示的可行性。针对fpga的特点,提出了几种加速稀疏矩阵向量乘法和L-BFGS迭代优化算法的技术。实验结果表明,在无玻璃显示应用中,我们达到了12.78X的整体加速。
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Session details: CAD Tools CPU-FPGA Co-Optimization for Big Data Applications: A Case Study of In-Memory Samtool Sorting (Abstract Only) Session details: Graph Processing Applications ASAP: Accelerated Short Read Alignment on Programmable Hardware (Abstract Only) Learning Convolutional Neural Networks for Data-Flow Graph Mapping on Spatial Programmable Architectures (Abstract Only)
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