5 key challenges in EDGE AI Development & how EDGENeural.ai is solving it

5 key challenges in EDGE AI Development & how EDGENeural.ai is solving it

Technology today has come a long way from calculators and computers. From phones to home lighting, television remotes to automobile consoles, there’s a surfeit of technology everywhere. The need and expectations of consumers have brought an evolution in computing enabling convenience, intelligence, and automation at the heart of every device today.

From personal computers back in the 90s where we hosted software and data locally to centralized services enabled by Cloud Computing in the late 2000s, we have experienced the paradigm shift in technology. This advancement in technology and computing has not only given a push to discover better but also provided an urge to innovate and progress further.

Now, we are moving into the world of Edge Computing where we store the data close to its source, cutting the need to even send it to the cloud every time for processing. Coupled with AI, Edge Computing can do wonders in our smart devices.

What is Edge AI?

Edge AI essentially means AI models are processed locally, on devices at the edge of a network and not a cloud. In contrast to other AI processes that are carried out via cloud-based datacenters, Edge AI hosts the AI model on the edge device.

So, in a nutshell, Edge AI enables abilities in devices to run data processing and data analytics locally on the device using data that they create so that they can make decisions in the form of output without the need to take the data to the cloud each time.

How far are we in Edge AI today?

According to Markets & Markets, the Edge AI hardware market is going to grow from 900 million units in 2021 to 2080 units in 2026. This surge is not only because of the consumer demand but also due to the abilities and opportunities offered by the deep learning models.

Some of the places that we can see Edge AI already being implemented today includes Self-Driving Cars, Home Assistants, Surveillance & Monitoring Systems, Industry IoT, Image & Audio Analytics.

We understand that Edge AI is proven and is here to stay for long. But how far have we progressed in implementing it? Are there challenges that still make us rethink the whole concept and what is the solution to these challenges? Let’s discuss. 

Challenges in Edge AI development

Although implementing Edge AI is very beneficial, it comes with its own challenges. There are many factors that intervene and make it hard to implement. Some of them include hardware & frameworks, optimization, integration, training models and deployment. In addition to these, it is also imperative for companies using Edge AI to keep up with the advancements in the market.

Some of the key deal-breaking challenges of Edge AI development today include:

  1. Lack of hardware standard – Edge computing not only needs sharpness in programming but also has a huge dependency on the hardware and support. Hardware in the market today doesn’t have standardized units. This makes Edge AI very difficult for companies to implement. In addition, there are many factors like power consumption, use cases, processors, memory requirements, etc. that need to be considered.
  1. Integration with various components – Hardware is just one aspect of the AI Model. While it is a general practice for developers to use different frameworks and models to build out the applications, seamless integration of these will be a tedious task. In addition, companies might also use third-party tools that need integration with the new hardware and software being used for Edge AI.
  1. Optimization – The deep neural networks might be computationally heavy and the Edge Devices might have constraints in running programs seamlessly. Thus optimization becomes a quintessential need to run the Edge AI models on the devices. Optimizations here will have to improve the performances at inference stages without affecting the accuracy.
  1. Limited expertise & lack of knowledge – AI is drastically evolving and changes in the industry are too dynamic. This calls for advancements that require resources working on AI technologies to be updated at all stages with the latest reforms and changes. From hardware selection to integration of tools & equipment, optimizing the model to deploying and testing, it needs expertise at different life cycle of the project. Edge AI is one of the latest advancements, the lack of knowledge and expertise in the field is overseen across all industries.
  1. Time & cost of development – It takes months together to build an Edge AI prototype and deploy it in production. This affects the go-to-market time for companies followed by costs incurred on the resources, R&D, training and finally hosting the applications. According to the insights from Glassdoor, the average salary of an engineer in the United States is $118,413 annually. This becomes a huge hurdle that many smaller and medium scale companies won’t be able to jump through.

How is EDGENeural.ai solving these challenges?

EDGENeural.ai is an Edge AI platform that helps developers leverage AI capabilities on edge devices and simplify their transformation journey. EDGENeural.ai works with the vision to decentralize AI. It provides a platform that enables developers a one-stop solution and helps companies cut down the costs and time required to go to market.

ENAP Studio by EDGENeural.ai is an end-to-end workflow solution, focused on improving the performance of AI algorithms and models for Edge devices. It has a fully-integrated modular workflow that allows developers to easily Train, Optimize and Deploy Edge AI Neural Networks.

Here is how the Train-Optimize-Deploy model enables seamless workflow from conceptualization stage to go-to-market product phase:

Training Models Using ENAP Studio – Using a simple UI-based training tool, developers can easily train their models by adding the link to their data set. The system automatically detects the configurations like the model, family name, etc. Advanced users can also customize the setting and train parameters for the particular data sets, hardware, etc.

Optimize Inference Performance –  ENAP Studio helps in reducing the computational heaviness through optimization. Developers can easily select models, choose hardware and quantization levels and run optimizations on their models. ENAP Studio does the heavy lifting and optimizes the model to reduce latency and redundancy.

Seamlessly Deploy On Edge Hardware – With ENAP Studio the deep learning models can be easily deployed with just few clicks. Developers can easily scale inference workloads, build, and optimize computer vision models for Edge devices. 

Want to learn more about ENAP Studio, check out the platform in detail here – https://edgeneural.ai/enap_studio/

With over 2000 million units in the next 5 years as predicted by Markets & Markets, the demand for EDGE adoption across industries is already seen. With more users using the devices running Edge AI, arises a greater number of challenges. To overcome these challenges, effectively manage the complete life cycle of the product and efficiently run Neural Networks, there is a need for a sustainable, scalable and trustable platform.

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