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May 12

Google App Engine

Authors: Pranesh Kulkarni; Shubham Deshmukh; Sakshi Kulkarni; Tejas Rajuskar Introduction Google App Engine (GAE) is a platform as a service (PaaS) solution for the development and deployment of web applications on the Google Cloud Platform (GCP). PaaS is a cloud computing model for the development and deployment of simple cloud-based applications…

Google Cloud Platform

8 min read

Google App Engine
Google App Engine

Jun 4, 2021

Power Electronics in Self-Driving Cars

Authors: Akshay Joshi, Soham Kamble, Atharva Karwande, Tejas Kolhe, Pranesh Kulkarni. — Automated vehicles have drawn increasing attention in recent years. These need sensors and computer processing which will understand the surrounding environment and make real-time decisions. However, these vehicles aren’t fully automated, and to reach higher levels of automation, a lot of sensors and systems should be implemented to control the…

Power Electronics

11 min read

Power Electronics in Self-Driving Cars
Power Electronics in Self-Driving Cars

Published in VLSI for Neural Networks and Artificial Intelligence

·Apr 28, 2021

Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration

Convolutional neural networks can also have accelerated by data path optimization. Early CNN accelerators have implemented Systolic arrays to perform fast and memory-efficient data operations. In this blog, we will discuss these systolic arrays in detail. CNNs are composed of a high number of convolution layers (155 in ResNet152). These…

Vlsi

3 min read

Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration
Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration

Published in VLSI for Neural Networks and Artificial Intelligence

·Feb 28, 2021

A VLSI DESIGN OF THE MINIMUM ENTROPY NEURON

In pattern recognition, the maximization of the transformation from the input to the output of the system corresponds to minimize the mean square error of the predicted output. For linear models, it is done by linear transformations. Generally, we encode the input data by using only the eigenvectors with the…

3 min read

A VLSI DESIGN OF THE MINIMUM ENTROPY NEURON
A VLSI DESIGN OF THE MINIMUM ENTROPY NEURON

Published in VLSI for Neural Networks and Artificial Intelligence

·Feb 17, 2021

NeuronLink: An Efficient Chip-to-Chip Interconnect for Large-Scale Neural Network Accelerators

Large-scale neural networks are deployed on the processors having multi-core structure and organized via network on chip for process the heavy neurons. These Neural network chips are connected via chip-to-chip interconnection networks to increase the efficiency of neuron processing. In th research, the author has proposed interchip and intrachip communication…

4 min read

NeuronLink: An Efficient Chip-to-Chip Interconnect for Large-Scale Neural Network Accelerators
NeuronLink: An Efficient Chip-to-Chip Interconnect for Large-Scale Neural Network Accelerators

Published in Radar Signal Processing

·Dec 20, 2020

Radar Signal Processing

History Before Radar Between the planet warfare, parabolic sound reflector were wont to provide early warning. Acoustic reflectors were also getting used . Although that they had finite success. and since of the growing speed of aircraft within the 1930s conveyed that they might already be too on the edge of influence by…

Radar

9 min read

Radar Signal Processing
Radar Signal Processing

Jun 2, 2020

Deploying a flask application on Heroku (Free Hosting Server)

Pranesh Kulkarni Step 1 : Creating Flask application : Write a code for a flask application. Sample code is given below import flask import werkzeug app = flask.Flask(__name__) @app.route(‘/’,methods=[‘GET’]) def hello(): return “<h1> Deployed to Heroku</h1>” if __name__==”__main__”: app.run() Save this file in working folder. e.g. flask_conn.py · Step 2…

Heroku

3 min read

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