Search Results for author:
Found 3 papers, 0 papers with code
Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications
Even though FPGA design exists long before the use of GPUs for accelerating computations and despite the fact that high-level synthesis (HLS) tools are getting more attractive, the adaptation of FPGAs for deep learning research and application development is poor due to the requirement of hardware design related expertise.
SqueezeJet: High-level Synthesis Accelerator Design for Deep Convolutional Neural Networks
Deep convolutional neural networks have dominated the pattern recognition scene by providing much more accurate solutions in computer vision problems such as object recognition and object detection.
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment
FPGAs are commonly used to accelerate domain-specific algorithmic implementations, as they can achieve impressive performance boosts, are reprogrammable and exhibit minimal power consumption.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
