Time Sharing Based Parallel Implementation of CNN on Low Cost FPGA

Dawwd, Shefa A.; Mahmood, Basil Sh.

doi:10.1109/icoase.2018.8548825

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some works target the design of DNN or RNN units on FPGA showing significant acceleration and power efficiency against conventional processor architecture [31,32]. To reduce the resources required for implementation, a time sharing based parallel implementation of CNN could be used [33] or optimized network operators [34]. For a deeper analysis, refer to the online tool [35] that compares NN accelerators on several hardware platforms (FPGA, ASIC and GPU) in terms of speed and power consumption.…”

Section: IVmentioning

confidence: 99%

Radar Signal Processing for Sensing in Assisted Living: The Challenges Associated With Real-Time Implementation of Emerging Algorithms

Kernec

Fioranelli

Ding

et al. 2019

IEEE Signal Process. Mag.

130

View full text Add to dashboard Cite

This article presents radar signal processing for sensing in the context of assisted living. This is covered through 3 example applications: human activity recognition for activities of daily living, respiratory disorder and Sleep Stages classification. The common challenge of classification is discussed within a framework of measurements/pre-processing, feature extraction, and classification algorithms for supervised learning. Then, the specific challenges of the 3 applications from a signal processing standpoint are detailed in their specific data processing and ad-hoc classification strategies, focusing on recent trends in the field of activity recognition (multidomain, multi-modal and fusion) and healthcare applications based on vital signs (super-resolution techniques) and commenting on outstanding challenges. To conclude, this paper explores the challenge of the real-time implementation of signal processing/classification algorithms.

show abstract

Section: IVmentioning

confidence: 99%