FPGA Accelerates Deep Residual Learning for Image Recognition

FPGA based Deep Learning Models for Object Detection and Recognition Comparison of Object Detection

DLAU: A Scalable Deep Learning Accelerator Unit on FPGA

FPGA based implementation of deep neural networks using on-chip memory

ADAS-Deep Learning Algorithms on FPGA

Automatic driver assistance system (ADAS) is one of the fastest growing segments in…

Challenges of FPGA based Implementation of Deep Learning Networks

Implementation of deep learning networks and, particularly for , CNNs on FPGAs has a large number of challenges. Some of the major challenges are: a)Requirement of a significant amount of storage .

Deep Learning Challenges and Solutions with Xilinx FPGAs

Deep learning algorithms have two distinct phases. First is a model training phase involves evaluating and modifying weights in a deep learning model using a set of labelled or even unlabeled data. The second phase is inference or…

Deep Learning Accelerator on FPGA Using Handwritten Digit Recognition

A Deep Learning Framework to Predict Routability for FPGA Circuit Placement

Parallel Dot-Products for Deep Learning on FPGA

Since many dot products can be calculated in parallel, as long as memory bandwidth is available, it is very important to implement this operation very efficiently to increase the density of MACC units in an FPGA. We propose an implementation of…