2 research outputs found
Visual, navigation and communication aid for visually impaired person
The loss of vision restrained the visually impaired people from performing their daily task. This issue has impeded their free-movement and turned them into dependent a person. People in this sector did not face technologies revamping their situations. With the advent of computer vision, artificial intelligence, the situation improved to a great extent. The propounded design is an implementation of a wearable device which is capable of performing a lot of features. It is employed to provide visual instinct by recognizing objects, identifying the face of choices. The device runs a pre-trained model to classify common objects from household items to automobiles items. Optical character recognition and Google translate were executed to read any text from image and convert speech of the user to text respectively. Besides, the user can search for an interesting topic by the command in the form of speech. Additionally, ultrasonic sensors were kept fixed at three positions to sense the obstacle during navigation. The display attached help in communication with deaf person and GPS and GSM module aid in tracing the user. All these features run by voice commands which are passed through the microphone of any earphone. The visual input is received through the camera and the computation task is processed in the raspberry pi board. However, the device seemed to be effective during the test and validation
Improving Performance Estimation for FPGA-based Accelerators for Convolutional Neural Networks
Field-programmable gate array (FPGA) based accelerators are being widely used
for acceleration of convolutional neural networks (CNNs) due to their potential
in improving the performance and reconfigurability for specific application
instances. To determine the optimal configuration of an FPGA-based accelerator,
it is necessary to explore the design space and an accurate performance
prediction plays an important role during the exploration. This work introduces
a novel method for fast and accurate estimation of latency based on a Gaussian
process parametrised by an analytic approximation and coupled with runtime
data. The experiments conducted on three different CNNs on an FPGA-based
accelerator on Intel Arria 10 GX 1150 demonstrated a 30.7% improvement in
accuracy with respect to the mean absolute error in comparison to a standard
analytic method in leave-one-out cross-validation.Comment: This article is accepted for publication at ARC'202