The latest technologies and sensors have played a key role in transforming the gadgets and devices we use into smart and intelligent devices. The increasing use of artificial intelligence (AI) has resulted in several social and economic benefits. With AI, computers and machines can analyze and learn from data faster than humans. An intelligence device can take decisions just like a human being can with the power of AI. The implementation of AI across diverse industries helps improve efficiency, productivity, and performance, from manufacturing to logistics. AI can be integrated into existing systems and processes to improve their functioning, scale them, reduce their costs, and enable them to make better decisions. Is there anything else that can improve artificial intelligence? Yes, embedded AI.
What is embedded AI?
- Generally, AI is viewed as a collection of algorithms that run on large cloud-based server platforms. These AI algorithms are used to analyze data and make predictions.
- The complex algorithms in AI are capable of processing huge volumes of data from cloud resources to transform existing business models.
- With the rise in technological advancements, recently, a new form of AI has emerged.
- This AI form is narrower in focus and has a limited reach. It is known as embedded AI since it is embedded in the graphics processing units (GPUs), systems on chips (SOCs), and session border controllers (SBCs).
- Since embedded AI exists on the device or system, there is less dependency on cloud resources for AI data processing.
- Embedded AI refers to the application of deep learning and machine learning in software occurring at the device level.
- On the basis of the data collected and analyzed, the software can be programmed to deliver both reactive and predictive intelligence.
AI systems have two primary responsibilities.
Learning
- It refers to the process of training the neural network based on the data collected.
- The data may be in the form of images, videos, or any other type based on information collected by sensors.
Inference
It is the process by which the trained neural network is able to recognize things and instances from real-world data.
There are drawbacks to the traditional AI architecture which embedded AI can help to overcome.
In the traditional approach, data is collected by end-to-end embedded devices, which is sent to the cloud-based server via the internet. The training and inference are carried out on the server. The output is returned to the device.
This process has many limitations, which can be effectively overcome using the embedded AI technology.
High latency
- Taking data to the server and waiting for the results takes time.
- The high degree of latency and delay in cases like automated vehicles or safety systems may be detrimental.
- Embedded AI involves processing data on the local system without needing it to be sent to a cloud server.
- This reduces the latency and results in faster data processing.
High cost
- Transmitting data from a device to the cloud server and back to the data increases the power consumption and is also data-intensive. This results in a high cost.
- In the case of embedded AI, the data can be processed locally on a device, and only the results need to be sent to the cloud-based storage.
- This would mean a significant reduction in the cost of data transfer.
Data transfer
- At present, the data collected by a sensor or device is sent to a cloud-based system for analysis.
- The results generated are then returned to the device for the necessary action. This causes an increase in data transfer.
- In the case of embedded AI, the device can process the data locally, sending only the results to the cloud results in a significant reduction in data transfer.
Security and privacy concerns
- When data is constantly sent to the cloud, there are higher chances of security threats and privacy concerns.
- In embedded AI, the data can be stored locally in a secure manner without the need to transmit it to a central cloud-based processor.
- Improved security mechanisms like facial recognition can be implemented on a local device.
- Avoiding data transmission can reduce the probability of security threats significantly.
Benefits of AI powered embedded systems
- Easier to manage since they consist of less resources.
- Automated processing of information
- Increased operational efficiency.
- Predictive maintenance for machinery and equipment.
- Improved risk management.
- Advanced analytics
- Business insights
Industries that have adopted embedded AI
There are several applications and uses of embedded AI. Here are the various industries where embedded AI has already been implemented.
- Healthcare
- Retail
- Supply chain
- Finance
- Agriculture
- Manufacturing
- Autonomous vehicles
How does embedded AI work?
- In the past few years, there has been a huge shift in the processing of data, results, and AI tasks from the cloud-level to the device level.
- Embedded AI has emerged due to this change that has taken place. Embedded AI implements machine learning models at the device level.
- It uses the results directly to take an appropriate action or perform a task.
- Here, the cloud system can still play a key role in the storage of data since the device can only store data temporarily.
- The embedded software enables the system to respond in real-time based on the data collected by the sensors and devices.
The use of embedded AI will facilitate the development of novel and cost-effective AI-powered solutions. Developing these solutions with the cloud-based AI system alone is impossible. Embedded AI needs expertise and knowledge beyond the traditional embedded systems, data science, and machine learning models. Knowledge about sensors, devices, sensors, and advanced, near-real-time signal processing methods for audio, video, motion, or other signals is a must. Sophisticated tools, technologies, and frameworks are needed to develop embedded AI apps. The market for embedded AI globally is expected to grow at a CAGR of 5.4% from 2018 to 2026, to reach $38.87 billion. Embedded systems powered by AI are only in the niche stage of adoption. There is still tremendous scope and potential for embedded AI to be implemented in various industries to solve real-world challenges.
