Tech

Making a neural network with neural chips and AI SDK: a tutorial for making your own design

Are you interested in programming a neural network? If so, then this tutorial is exactly what you need. We will walk you through the process of creating and configuring a customized artificial intelligence (AI) system with advanced neural chips and AI software development kits (SDKs). In this blog post, we will provide step-by-step instructions to help you setup your own AI platform from scratch. With our guidance, it won’t take long for you to get up-and-running with your very own powerful AI system using state-of-the-art tools provided by both hardware companies and software developers.

Published

2 years ago

December 15, 2022

Adva

Designing and training your own custom neural network can be daunting, but it can be surprisingly easy with the correct tools. This tutorial will give you guidance on how to use neural chips and an AI SDK to design and train your own neural network without needing deep learning expertise.

You will need a few things to make your own neural network with neural chips and AI SDK. First, you will need a computer with a good CPU and enough RAM to handle the data processing involved in training a neural network. You will also need a sufficiently large dataset to train your neural network. Finally, you will need the AI SDK, which can be downloaded for free from many places online.

Designing the Neural Network

When designing a neural network, there are a few key things to keep in mind. First, you need to determine the number of input and output nodes. Second, you need to choose an appropriate activation function for each layer. And third, you need to select a suitable optimization algorithm.

Fortunately, there are many resources available that can help with this process. For example, the Neural Network Toolbox in MATLAB provides functions for creating and training neural networks. There are also many online tutorials that can walk you through the process step-by-step.

Once you’ve designed your neural network, it’s time to train it. This is where the AI SDK comes in. The SDK offers a number of different algorithms for training neural networks, including backpropagation and stochastic gradient descent. You can also use the SDK to monitor the training process and ensure that the network converges toward a solution.

After training is complete, you can use the neural network for inference. This means using it to make predictions on new data points. The AI SDK provides several different ways to do this, including using a trained model directly or exporting a model to ONNX format and using it in another tool such as TensorFlow or PyTorch.

Making a neural network with neural chips and AI SDK is a great way to get started with SDK deep learning. By following this tutorial, you’ll be able to design and train your own neural network in no time.

This tutorial guide will teach you how to configure the AI SDK on your machine to create a neural network. We’ll walk you through obtaining the necessary software, importing formats and data, and generating an example neural network that performs a basic task.

First, you will need to download the AI SDK from the Intel website. Once you have downloaded the SDK, you must install it on your computer.

Once the SDK is installed, you will need to open a new project in the SDK. To do this, go to File > New > Project. In the project window, select “Neural Network” as the project type.

Once you have created your new project, you need to add input data. To do this, go to Data> Add Data. In the data window, select “Input Data” as the data type. Then, click the “Add” button and select an image file from your computer. The image file should be in .png format and should be 28×28 pixels in size.

After you have added your input data, you will need to add some output data. To do this, go to Data> Add Data again. In the data window, select “Output Data” as the data type. Then, click on the “Add” button and select an image file from your computer that contains the desired output for your neural network. The output image should also be in .png format and 28×28 pixels in size..]

neural network

Making a Neural Network with Neural Chips

If you’re interested in creating your own neural network designs, you can do so with the help of neural chips and AI SDK.

First, you’ll need to obtain a neural chip. You can get these for sale online or at select electronics stores. Once you have your chip, install it on your computer according to the manufacturer’s instructions.

Next, download and install the AI SDK onto your computer. This software will allow you to program your neural chip with the desired functionality.

Once the AI SDK is installed, open it up and create a new project. Select “neural network” as the project type and name your project accordingly.

Now it’s time to begin coding your neural network! The first thing you’ll need to do is define the input layer. This is where you’ll specify the number of neurons in the input layer and what type of data they will be processing (e.g., images, numbers, etc.).

By defining the input layer, you’ll find it easy to determine the hidden layers. The number of neurons in each hidden layer will depend on how complicated you want your neural network design to be. For this tutorial, we’ll use two hidden layers consisting of 16 neurons each.

Finally, you’ll need to define the output layer. This is where you’ll specify the number of neurons in the output layer and the type of data they will output (e.g., classification results, probabilities, etc.).

Once you’ve defined all three layers, you can begin coding their connections. Each connection will have a weight associated with it which determines how strong the connection is. You can randomly initialize the weights or specify them yourself.

After all the connections have been coded, you’ll need to specify the activation function for each neuron. This function determines how the neuron will “fire” when it receives input from the previous layer.

There are many different activation functions to choose from, but we’ll use the sigmoid function for this tutorial.

Once all the neurons have been activated, your neural network is ready to run! You can test it on sample data to see how well it performs.

Training the Neural Network

Once you have your neural chips and AI SDK, it’s time to train your neural network. This is where the real power of neural networks comes into play. By training your neural network, you can make it smarter and more efficient at completing tasks.

There are a few different ways to train your neural network. You can use supervised learning, unsupervised learning, or reinforcement learning. Supervised learning is when you provide the neural network with input data and corresponding output data. The neural network then learns to map the inputs to the outputs. Unsupervised learning is when you provide the neural network with input data but not corresponding output data. The neural network then has to learn to find patterns in the data. Reinforcement learning is when you provide the neural network with a goal, and it has to learn how to achieve that goal.

Whatever method you choose, training your neural network will take time and effort. But it’s worth it because once your neural network is trained, it can do some amazing things!

Making a Neural Network with AI SDK

If you’re looking to make your own neural network with AI SDK, there are some things you must keep in mind. First, you’ll need to select the proper neural chips for your design. There are many different chips in the market, so finding one perfect for your needs is significant.

Once you’ve selected the correct chip, you’ll need to configure it correctly. This includes setting up the proper connection between the chip and your computer. You’ll also need to set the correct parameters for training and inference.

After configuring your chip, you can use AI SDK to train and test your neural network. AI SDK provides various tools that make developing and deploying neural networks easy. With AI SDK, you can experiment with different network architectures and hyperparameters to find the best-performing model for your data.

The Different Types of Neural Networks

There are three main types of neural networks: supervised, unsupervised, and reinforcement learning. Supervised neural networks are trained on an already established system, like images with labels identifying the objects in them. Unsupervised neural networks are trained using unlabeled data, such as images. Reinforcement learning neural networks are trained using a feedback signal, such as a reward or punishment, to learn which actions lead to the best outcomes.

If you want to begin building your own neural networks, this tutorial is for you. We’ll show you how to use neural chips and AI SDKs to create your own design. With the correct tools and a little creativity, you’ll be able to make a neural network that’s uniquely yours. So, what are you waiting for? Get started today and see what you can create!

Tech

The Speed of Sight: Low-Latency HEVC for Military Situational Awareness

Transmitting high-quality video in military operations is no small feat, especially in high-stakes environments where every second counts. Enter low-latency HEVC, a technology transforming how real-time video is delivered and processed on the battlefield. By compressing video efficiently without sacrificing clarity, HEVC ensures seamless situational awareness, empowering faster decision-making and improved mission coordination. This breakthrough is critical for modern military strategies, where accurate and instant information can mean the difference between success and failure. Discover how HEVC is redefining operational effectiveness.

Published

2 weeks ago

March 21, 2025

Adva

In the high-stakes theater of modern warfare, information dominance hinges on the ability to rapidly acquire, process, and disseminate critical intelligence. Real-time video feeds from drones, surveillance platforms, and soldier-worn cameras are indispensable tools, but their effectiveness is inherently tied to the latency of video encoding and decoding. This is where low-latency High Efficiency Video Coding (HEVC) encoders and decoders are proving transformative for military situational awareness.

The Latency Challenge in Military Video Transmission

Military operations often demand immediate access to visual information. Delays in video transmission can lead to critical misinterpretations, missed opportunities, and compromised safety. Traditional video encoding and decoding methods, particularly those relying on cloud-based processing, introduce significant latency due to network congestion and processing overhead.

High-Efficiency Video Coding (HEVC)

HEVC and Low-Latency Encoding: A Game Changer

HEVC, also known as H.265, offers superior compression efficiency compared to its predecessors, allowing for higher-quality video transmission at lower bitrates. However, achieving low latency with HEVC requires specialized encoding and decoding techniques.

Optimized Encoding Parameters:

By carefully adjusting encoding parameters, such as GOP (Group of Pictures) structure, frame rate, and quantization parameters, encoders can minimize processing delays and reduce latency.

Slice-Based Encoding and Decoding: Dividing video frames into smaller slices allows for parallel processing, reducing encoding and decoding times.

Predictive Encoding and Decoding Techniques:

Advanced predictive algorithms can anticipate future frames, reducing the amount of data that needs to be transmitted and processed.

Hardware Acceleration:

Utilizing dedicated hardware accelerators, such as GPUs and specialized ASICs, can significantly speed up HEVC encoding and decoding.

Military Situational Awareness Applications:

Real-Time Drone Surveillance:

Low-latency HEVC enables commanders to receive near-instantaneous video feeds from drones, providing tactical situational awareness for tactical decision-making.

Soldier-Worn Camera Systems:

Real-time video feeds from soldier-worn cameras enhance situational awareness for ground troops, enabling rapid threat assessment and improved coordination.

Remote Battlefield Monitoring:

Low-latency HEVC allows for remote monitoring of critical infrastructure and strategic locations, providing early warning of potential threats.

Target Acquisition and Tracking:

Real-time video feeds from surveillance platforms enable rapid target acquisition and tracking, improving the accuracy of weapon systems.

Command and Control (C2) Systems:

Low-latency HEVC integrates seamlessly with C2 systems, providing commanders with a comprehensive and up-to-date view of the battlefield.

Remote Medical Assistance:

In situations where a medic cannot be on site, a medical professional can view a live low latency feed, and provide real time instructions for the treatment of wounded soldiers.

Benefits for Military Operations:

Enhanced Real-Time Situational Awareness:

Low-latency HEVC provides commanders with near-instantaneous access to critical visual information.

Improved Decision-Making:

Reduced latency enables faster and more accurate decision-making in dynamic and time-sensitive situations.

Increased Operational Effectiveness:

Real-time video feeds enhance coordination, improve target acquisition, and reduce response times.

Enhanced Soldier Safety:

Near real time feeds help to prevent ambushes, and other dangers.

The Future of Low-Latency HEVC in Military Applications:

As military technology continues to evolve, the demand for low-latency video transmission will only increase. Future developments will focus on:

Further reducing latency through advanced encoding and decoding techniques.

Developing more robust and resilient video transmission systems for harsh environments.

Integrating AI-powered video analytics with low-latency HEVC to enhance situational awareness.

Improving the bandwidth efficiency of HEVC to enable transmission over limited networks.

Low-latency HEVC encoders and decoders are revolutionizing military situational awareness, providing commanders with a decisive advantage in the modern battlespace. By enabling real-time access to critical visual information, these technologies are playing a vital role in ensuring the safety and effectiveness of military operations.

Business Solutions

Luxury Real Estate in Ra’anana, Israel

Discover the allure of Luxury Real Estate in Ra’anana, Israel, where modern elegance meets serene suburban charm. Renowned for its upscale homes, beautifully landscaped neighborhoods, and vibrant community, Ra’anana offers a premier lifestyle. Explore sophisticated villas and exclusive residences in one of Israel’s most sought-after locations, perfect for refined living.

Published

2 weeks ago

March 20, 2025

Marks Strand

Raanana: A Haven for Luxury Living in Israel

Raanana, a charming city nestled just north of Tel Aviv, has become a haven for luxury living in Israel. Renowned for its upscale atmosphere, lush greenery, and excellent quality of life, Raanana attracts discerning homebuyers seeking a sophisticated and tranquil environment.

The Raanana Luxury Real Estate Market

The Raanana luxury real estate market is characterized by a diverse range of properties, from modern villas and spacious apartments to historic mansions and exclusive gated communities. These properties often feature high-end finishes, private gardens, swimming pools, and breathtaking views.

Popular Locations for Luxury Real Estate in Raanana

Several areas in Raanana have emerged as hotspots for luxury real estate buyers:

Neve Zemer: This prestigious neighborhood is home to some of the most sought-after properties in Raanana, featuring large villas, private gardens, and a tranquil atmosphere.
The City Center: The heart of Raanana offers a vibrant mix of modern high-rises and renovated historic buildings, providing a cosmopolitan lifestyle with easy access to amenities and entertainment.
The Green Areas: Raanana boasts numerous parks and green spaces, and properties located near these areas offer a serene and peaceful living environment.

Luxury Real Estate in Ra'anana

Key Considerations for Luxury Home Buyers in Raanana

If you are considering purchasing luxury real estate in Raanana, there are several factors to keep in mind:

Budget: The cost of luxury real estate in Raanana can vary significantly depending on the location, size, and amenities of the property.
Lifestyle: Consider your lifestyle and how it aligns with the location and amenities of the property. For example, if you are looking for a family-friendly neighborhood, Neve Zemer might be a good option.
Investment Potential: Raanana is a stable and growing city, making it a sound investment for luxury real estate buyers.

The Appeal of Raanana

Raanana’s appeal stems from several factors:

High Quality of Life: Raanana offers excellent schools, a wide range of cultural and recreational activities, and a safe and welcoming community.
Proximity to Tel Aviv: Raanana is conveniently located just a short drive from Tel Aviv, providing easy access to the city’s vibrant cultural and business scene.
Green Spaces and Parks: Raanana boasts numerous parks and green spaces, offering residents a tranquil and relaxing environment.

Raanana offers a unique and sophisticated lifestyle for luxury homebuyers in Israel. With its diverse range of properties, excellent amenities, and tranquil atmosphere, Raanana is an ideal choice for those seeking a high-quality living experience.

Additional Resources:

Business Solutions

Shielding the Modern Fleet: Robust Vehicle Cybersecurity Strategies in the Age of Connectivity

In the age of smart vehicles and constant connectivity, cybersecurity has become a critical concern for fleet managers and car manufacturers alike. Modern vehicles are packed with advanced technology, making them both efficient and vulnerable to cyber threats. This blog dives into the importance of robust vehicle cybersecurity, exploring cutting-edge strategies to safeguard connected fleets against potential risks. Whether you oversee commercial fleets or personal vehicles, understanding these strategies is key to staying one step ahead of evolving cyber threats. Stay secure, stay connected, and take control of your vehicle’s safety!

Published

2 weeks ago

March 19, 2025

Adva

The modern vehicle, a marvel of interconnected systems, is increasingly vulnerable to cyber threats. As cars transform into rolling computers, a comprehensive cybersecurity strategy is no longer optional—it’s imperative. OEMs must adopt a multi-layered approach to protect vehicles from evolving threats, encompassing everything from secure design principles to automated threat detection in the automotive cybersecurity sector.

Building Security In: The Foundation of a Robust Strategy

The first line of defense lies in embedding security into the vehicle’s architecture from the ground up. This involves:

Secure Design Principles: Implementing security by design, ensuring that security considerations are integrated into every stage of the development lifecycle. This includes threat modeling, vulnerability assessments, and secure coding practices.

Hardware Security Modules (HSMs): Utilizing dedicated hardware modules to protect sensitive cryptographic keys and perform secure operations. This provides a secure foundation for authentication, encryption, and other security functions.

Secure Communication Protocols: Employing robust communication protocols, such as encrypted CAN bus communication and secure Ethernet, to protect data transmitted within the vehicle’s network.

Secure Boot and Firmware Updates: Implementing secure boot mechanisms to ensure that ECUs boot from trusted firmware and utilizing secure over-the-air (OTA) update capabilities to patch vulnerabilities and deploy security enhancements.

Vehicle Cybersecurity

Automated Threat Detection: TARA Cybersecurity and Beyond

As the complexity of vehicle systems increases, manual threat analysis becomes impractical. Automated threat detection, particularly through Threat Analysis and Risk Assessment (TARA) for cybersecurity, is essential for proactive defense.

TARA Cybersecurity:

This methodology involves systematically identifying and assessing potential threats to the vehicle’s cybersecurity.

It uses automated tools to analyze system architectures, identify potential attack vectors, and assess the impact of potential attacks.

TARA enables manufacturers to prioritize security measures and allocate resources effectively.

This process should be continuously repeated throughout the vehicles life cycle, as new threats emerge.

Intrusion Detection and Prevention Systems (IDPS):

Deploying IDPS solutions that monitor vehicle networks for anomalies and suspicious activity.

These systems can use machine learning algorithms to detect subtle threats that traditional signature-based systems might miss.

Security Information and Event Management (SIEM):

Centralizing security logs and events from various vehicle systems to provide a comprehensive view of the vehicle’s security posture.

SIEM systems can correlate events and identify patterns that indicate potential attacks.

Vulnerability Scanning and Penetration Testing:

Regularly conducting automated vulnerability scans and penetration tests to identify and address security weaknesses.

This proactive approach helps to discover vulnerabilities before malicious actors can exploit them.

Beyond the Vehicle: A Holistic Approach

A comprehensive cybersecurity strategy extends beyond the vehicle itself, encompassing the entire ecosystem:

Supply Chain Security: Ensuring that all components and software used in the vehicle are sourced from trusted suppliers and undergo rigorous security testing.

Cloud Security: Protecting data transmitted to and from the cloud, as well as securing cloud-based services used by the vehicle.

Incident Response: Developing a robust incident response plan to handle cybersecurity incidents effectively, including containment, eradication, and recovery.

Collaboration and Information Sharing: Fostering collaboration and information sharing among manufacturers, suppliers, and cybersecurity experts to stay ahead of evolving threats.

Vehicle cybersecurity is an ongoing process that requires a multi-faceted approach. By combining secure design principles, automated threat detection, and a holistic security strategy, manufacturers can help to protect vehicles from cyberattacks and ensure the safety and security of drivers and passengers in the connected age.