2,549 research outputs found
FFT-Based Deep Learning Deployment in Embedded Systems
Deep learning has delivered its powerfulness in many application domains,
especially in image and speech recognition. As the backbone of deep learning,
deep neural networks (DNNs) consist of multiple layers of various types with
hundreds to thousands of neurons. Embedded platforms are now becoming essential
for deep learning deployment due to their portability, versatility, and energy
efficiency. The large model size of DNNs, while providing excellent accuracy,
also burdens the embedded platforms with intensive computation and storage.
Researchers have investigated on reducing DNN model size with negligible
accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN
training and inference model suitable for embedded platforms with reduced
asymptotic complexity of both computation and storage, making our approach
distinguished from existing approaches. We develop the training and inference
algorithms based on FFT as the computing kernel and deploy the FFT-based
inference model on embedded platforms achieving extraordinary processing speed.Comment: Design, Automation, and Test in Europe (DATE) For source code, please
contact Mahdi Nazemi at <[email protected]
Automatic generation of hardware Tree Classifiers
Machine Learning is growing in popularity and spreading across different fields for various applications. Due to this trend, machine learning algorithms use different hardware platforms and are being experimented to obtain high test accuracy and throughput. FPGAs are well-suited hardware platform for machine learning because of its re-programmability and lower power consumption. Programming using FPGAs for machine learning algorithms requires substantial engineering time and effort compared to software implementation. We propose a software assisted design flow to program FPGA for machine learning algorithms using our hardware library. The hardware library is highly parameterized and it accommodates Tree Classifiers. As of now, our library consists of the components required to implement decision trees and random forests. The whole automation is wrapped around using a python script which takes you from the first step of having a dataset and design choices to the last step of having a hardware descriptive code for the trained machine learning model
Performance Evaluation of Neural Networks for Animal Behaviors Classification: Horse Gaits Case Study
The study and monitoring of wildlife has always been a subject of great
interest. Studying the behavior of wildlife animals is a very complex task due to
the difficulties to track them and classify their behaviors through the collected
sensory information. Novel technology allows designing low cost systems that
facilitate these tasks. There are currently some commercial solutions to this problem;
however, it is not possible to obtain a highly accurate classification due to the
lack of gathered information. In this work, we propose an animal behavior recognition,
classification and monitoring system based on a smart collar device provided
with inertial sensors and a feed-forward neural network or Multi-Layer Perceptron
(MLP) to classify the possible animal behavior based on the collected sensory
information. Experimental results over horse gaits case study show that the recognition
system achieves an accuracy of up to 95.6%.Junta de Andalucía P12-TIC-130
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