2016 International Conference on Field-Programmable Technology (FPT) 2016
DOI: 10.1109/fpt.2016.7929192
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Cited by 240 publications
(123 citation statements)
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“…Recently, heterogeneous hardware platforms present the potential to accelerate specific deep learning algorithms while reducing the processing time and energy consumption [53], [54]. For the hardware on EI, various heterogeneous hardware are developed for particular EI application scenario to address the resource limitation problem in the edge.…”
Section: Hardwarementioning
confidence: 99%
“…Recently, heterogeneous hardware platforms present the potential to accelerate specific deep learning algorithms while reducing the processing time and energy consumption [53], [54]. For the hardware on EI, various heterogeneous hardware are developed for particular EI application scenario to address the resource limitation problem in the edge.…”
Section: Hardwarementioning
confidence: 99%
“…Although this paper presents an FPGA implementation, the pipelined design proposed in this work is not restricted to FPGA designs. FPGAs are popularly adopted in various applications such as cloud servers and IoT platforms [8]; hence studies on FPGA-specific implementation, such as [9], are interesting. However, this study focuses on the design at the abstraction level of carry-save adder (CSA) trees [10] and relies on the technology mapper of the FPGA design tool, so that the design can be adopted in other implementation technologies.…”
Section: Introductionmentioning
confidence: 99%
“…Compared to CPUs, the neural network's accelerators based on FPGA are increasingly popular because of their higher efficiency [14]. Over the baseline CPU, FPGA accelerators deliver one to two orders of magnitude speedups [1].…”
Section: Introductionmentioning
confidence: 99%
“…Over the baseline CPU, FPGA accelerators deliver one to two orders of magnitude speedups [1]. Moreover, the FPGA1024 design delivers almost 50x performance improvement over the baseline CPU (Intel5 Xeon E5-2699v3 server) [14]. In addition, due to the structure of CNN, 2 International Journal of Reconfigurable Computing each layer of calculation is independent of others, and the interlayered structure can be dealt with like a flow structure.…”
Section: Introductionmentioning
confidence: 99%