… FPGAs for hardware acceleration of deep learning algorithms. Indeed, like software approaches,
FPGA is … To provide the best prediction with deep learning, it is worth evaluating a lot of …

… (DNNs), have resulted in highly accurate predictions in image classification and object
detection applications [1], [2]. However, the great success in CNN comes with the cost of …

… In this paper, we systematically investigate the neural network accelerator based on Field-…
form quantization) with negligible loss of prediction accuracy. The compressed model is then …

… The speed of different platforms can be measured by latency, which in this paper represents
the execution time required to predict a word. Energy efficiency is the amount of energy …

… of machine learning was mainly based on simple and efficient classification methods such as
support vector machine (… We propose two architectures to accelerate the networ= prediction …

… in machine learning over traditional algorithms based on … work on neural network inference
accelerators based on FPGA and … a trained model to predict or classify new data. The training …

… of hardware accelerators based on intellectual property (IP) cores are regarded as … It should
be time predicted to perform real-time responses and execution for big data applications. It …

… and an accurate performance prediction plays an important … method for fast and accurate
estimation of latency based on … of this method in comparison to other machine learning (ML) …

… the predicted results since we have included the prediction … of YOLOv2 accelerator based
on Zynq7000 FPGA … real-time machine learning, deep neural network acceleration, and low-…

… Therefore, using FPGA to accelerate the inference process of deep learning is of high … can
reduce computational complexity without loss of prediction accuracy. The fixed-point network …