Model Zoos Set to Simplify Edge AI Application Development
Join us as we delve into how model zoos and deep learning will simplify edge AI application developement
Model Zoos are coming! And they’re going to speed up the avalanche of new edge devices. So, what exactly are Model Zoos?
Zoos provide collections of data models that perform functions, like voice activation and environment sensing. Developers then mix and match the models in a freeform fashion and create an avalanche of game-changing AI edge applications.
Generative artificial intelligence and edge computing have the potential to dramatically improve how information flows, work gets done, products are designed, and customers are serviced. However, to reap such benefits, AI edge application development must become simpler – and that’s where model zoos come in.
But first, it’s important to note three technology trends that are coalescing and creating both opportunities and challenges for different businesses.
The Edge Changes Data Processing
Firstly, edge computing has been gaining traction because of its hardware design, processing capabilities, and cloud efficiency. Edge computing solutions come in all shapes and sizes, including small special purpose sensors, embedded modules, and devices.
The key difference is, they include processing power and are smart enough to process data autonomously. As a result, they have the ability to move a significant portion of the data processing task away from large centralized cloud data centers and closer to where the information is being gathered and used. By making that change, they lower cloud bandwidth usage and costs while speeding up response time.
Generative AI’s Limitless Applications
Secondly, Generative AI, empowers organizations to sift through large volumes of information, correlate data points, make deductions, and automate manual actions.
Using AI models, companies can train AI software to perform just about any task, like recognizing people’s faces via computer vision, or detecting the proximity of objects using wireless.
Model Zoos Simplify Edge AI Application Development
To make it a trifecta, there’s emergence of zoos, which are repositories, libraries, or marketplaces that house pre-trained AI models.
The models in the zoos are designed to have open interfaces and are readily available, so they can be downloaded and deployed for any use case on different computer platforms. They provide needed flexibility in the cloud by making applications or at least the building block for AI applications more interchangeable and requiring less custom development.
The potential innovations and benefits are evident in virtually every industry, from healthcare to mining. Their usage and positive impact on organization are now limited more by corporate imaginations than technical limitations, except for one.
Edge AI Models Severely Lacking Now
“Building edge AI applications for low-powered devices is quite difficult as things stand. Model Zoos are set to change that.”
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Creating essential edge AI functions, or building blocks, is exceedingly complicated because functions, like AI Vision, Speech to Text, etc. must be designed from the ground up for each hardware platform they are to run on. This process requires a very deep understanding of embedded systems and firmware, and a lot of hardware experience.
Nowadays, no one writes their entire application themselves from scratch. Instead, developers want to and expect to be able to leverage preconfigured application building blocks, such as security authentication software, voice recognition, face recognition etc. They do not want to have to develop the needed low-level functions themselves.
Unfortunately, such components largely do not yet exist for edge/AI applications. Therefore, businesses are in a waiting game, unless they have the bandwidth and expertise to generate the entire application infrastructure themselves, starting from the microprocessor to the network and then to the application layer.
Edge AI Application Building Blocks
Over time, people creating intelligent edge applications will be able to tap into a growing body of small [language] models that perform very specific functions and have been optimized for the one or more AI-ready MCUs from different vendors.
Here are the main categories:
- Speech Recognition: Translate spoken language into text and vice versa.
- Location Awareness: Determine a device’s geographical position and direction.
- Vision: Recognize still and moving images for what they are, or recognize individuals.
- Environmental Sensing: Collect data, like temperature, humidity and surroundings.
- Sound Identification: Recognize sounds, languages, even individual voices.
- Natural Language: Puts words into context and understands the speaker’s intent.
- Gesture Recognition: Identify specific movements, posture, gait.
- Biometric Authentication: Verify individuals’ identity via fingerprints or facial recognition.
- Emotion Recognition: Analyze expressions and tone to assess how individuals are feeling.
- Anomaly Detection: Identify unusual patterns, outliers, among large volumes of data.
- Time Series Analysis: Analyze data points sequenced over time to identify trends.
- Predictive Analytics: Utilize pre-trained models to foretell how equipment will operate.
- Augmented Reality: Delivers rich, interactive visual and spatial data experiences.
- Multimodal Interaction: Reach conclusions from multiple sensory inputs.
- Pattern Mining: Identify sequences or patterns in data, in their order or across time.
Even when such functions are available and ready made, innovators of smart devices have much more on their plate to consider. They must carefully balance the functionality against the resources they have available for the chosen platform.
The new applications they are dreaming of will need to run on new small, special purpose, low-power chips. These latest MCUs and integrated Neural Processors typically have small form factors that limit their computational resources, like memory, bandwidth, and power.
Edge AI Modules are Needed
Consequently, companies cannot simply port AI applications that run on their large cloud systems with unlimited compute to constrained devices at the edge. Not now, not ever. It’s no easy feat porting an AI Model optimized for one edge device to another device.
Why? The AI-native MCU and firmware driving it are so closely coupled, that most models will require significant adaptation for each MCU.
Regardless of their industry, one hurdle most businesses face is they do not have edge AI expertise to create the essential functions they need i.e. “creating new models on proprietary chipsets”. That’s the sort of work only a small number of embedded systems companies in the world are capable of doing well. So, how are model zoos simplifying edge AI app development?
How Model Zoos are Simplifying Edge AI App Developement
1. Shrinking Product Life Cycles
AI is gaining traction and impacting businesses. Consequently, product life cycles continue to shrink and businesses face significant competitive pressure to get in and out of markets quickly.
Nowadays, organizations do not want to get bogged down in long term projects, like developing internal edge AI application development expertise. They need to be able to bring products to market more quickly.
Chip vendors themselves are in specialized niches. They need to limit the number of people that they work closely with because their resources are constrained. They prefer working with a select list of embedded systems partners because they have the expertise needed to develop models optimized for the different platforms.
2. Application Development Simplified to a Degree
What businesses need is simple Edge AI application development, something akin to the almost plug and play world now being made possible by Zoos of models for cloud solutions. Similar solutions for Edge AI will begin to provide such capabilities.
They will streamline a lot of the development process, depending on the application. If a business wants to use a model on the same platform it was originally designed for, but with a different objective in mind, that will work.
On the other hand, for example, say someone has an object recognition model that distinguishes say sheep from goats, but they want to use it on a different MCU. They might have to do as much as 40% of the work, all over again.
3. Support Edge Device Heterogeneity
Edge device heterogeneity presents challenges to developers. These products range from high-powered industrial machines to simple IoT sensors, each with their own unique hardware, software, and performance constraints.
The hope is Edge AI model zoos will deliver solutions that run on hardware with different base architectures, like CPUs, GPUs, FPGAs, and specialized AI accelerators. With it, once a business builds edge AI software, they can deploy it wherever it is needed.
4. Overcome Data Privacy and Security Concerns
Nowadays, cybersecurity and data privacy are important application development considerations. Businesses and consumers want to be certain that their information will be protected and used appropriately.
Since edge devices often process sensitive information locally, edge AI models have to be not only efficient but also secure. The zoos and their models must ward off attacks from outsiders and operate securely even when part of a system may be compromised.
Increasingly, the government is becoming involved in application design. Legislation has been evolving and putting more regulations on how companies store and use customer information. Laws, like the European Union’s General Data Protection Regulation and California Consumer Privacy Act, mandate that companies disclose how information will be used and provide them with a way to opt out of select activities, if they want.
So from the ground up, edge AI applications must include sophisticated security techniques, like federated learning. In this case, models are trained across multiple decentralized devices but never transfer raw data, thus enhancing privacy.
5. Zoos and Models Speed Up Application Development
Developers and businesses can use zoos to accelerate their AI deployment. These models have been trained on large datasets and are designed to perform tasks, like image recognition, natural language processing, and predictive analytics. Model zoos enable businesses to deploy AI solutions quickly and efficiently, without the need for extensive data collection, model construction, and model training from scratch.
The value that Edge AI model zoos bring is creating a development environment where companies mix and match pre-trained modules, much like a web designer enriches a website with new features through WordPress plugins.
In sum, Edge AI Model Zoos could lower the barrier to application development by cutting out 80% of the work necessary to create an intelligent edge application. With them, companies will be able to devote more attention to their application and avoid struggling to make small language models work on the hardware in the first place.
The embedUR Difference
Companies would like to adopt models and zoos ASAP, but as noted, AI edge technologies are in an early stage of development. Consequently, many desired pieces are now missing, leaving developers with three options. They can create the functions they need for themselves (if they have the expertise). They can wait for the silicon vendors to offer SDKs they can use. But while they wait, new business opportunities will be lost.
OR they can partner with embedded systems firms who have the knowhow to develop software directly for those chips. Such companies collaborate closely with silicon vendors, network equipment vendors, software suppliers, and AI experts.
They have the expertise needed to deliver Edge AI solutions today, in the absence of pre-existing models. These embedded systems specialists can build those models from scratch or pull functions together and adapt them for new needs, thus accelerating product development and reducing your time to market.
embedUR is one such company breaking ground at the edge – the new era of network evolution. We work closely with chip vendors and can help your organization realize Edge AI’s potential in your next dream product.
Contact us, to de-risk your Edge AI project and accelerate time to market.
