How to use edge AI to discover new opportunities?

For start-ups and large enterprises alike, committing to new and transformative technologies is critical to ensuring current and future competitiveness. Artificial intelligence (AI) provides multifaceted solutions to an increasingly wide range of industries.

In the current economic climate, R&D must be more fully funded than ever before. Businesses often look back on investments in future technology and infrastructure, while the risk of failure puts significant pressure on project stakeholders.

However, this does not mean that innovation should stop or even slow down. For start-ups and large enterprises alike, committing to new and transformative technologies is critical to ensuring current and future competitiveness. Artificial intelligence (AI) provides multifaceted solutions to an increasingly wide range of industries.

Over the past decade, artificial intelligence has played a significant role in unlocking entirely new revenue opportunities. From understanding and predicting user behavior to assisting in generating code and content, the artificial intelligence and machine learning (ML) revolution has exponentially increased the value consumers get from their apps, websites, and online services.

However, this revolution has been largely limited to the cloud, where virtually unlimited storage and compute, as well as convenient virtual hardware from major public cloud service providers, make it possible for every AI/ML application to Establishing best practice models becomes relatively easy to imagine.

AI: Moving to the Edge

Since AI processing primarily occurs in the cloud, the AI/ML revolution remains out of reach for edge devices. These are the smaller, lower-power processors found on factory floors, construction sites, research labs, nature reserves, on the accessories and clothes we wear, inside the packages we ship, and in any other environment where connectivity is needed, Storage, computing, and energy are limited or cannot be taken for granted. In their environment, compute cycles and hardware architecture matter, and budgets are measured not in the number of endpoints or socket connections, but in watts and nanoseconds.

CTOs, engineering, data and machine learning leaders, and product teams looking to break the next technology barrier in AI/ML must look to the edge. Edge AI and edge ML present unique and complex challenges that require careful coordination and engagement of many stakeholders with broad expertise ranging from system integration, design, operations and logistics to embedded, data, IT and ML engineering. expertise.

Edge AI means that algorithms must run in some kind of specific purpose hardware, from high-end gateways or local servers to low-end energy harvesting sensors and MCUs. Ensuring the success of such products and applications requires data and ML teams to work closely with product and hardware teams to understand and consider each other's needs, constraints, and requirements.

While the challenges of building custom edge AI solutions are not insurmountable, platforms exist for edge AI algorithm development that can help bridge the gap between the necessary teams and ensure higher levels of achievement in less time. of success and validate the direction for further investment should be made. Here are other things to note.

Testing Hardware While Developing Algorithms

Having data science and ML teams develop algorithms and then passing them to firmware engineers to install them on devices is neither efficient nor always It is possible. Hardware-in-the-loop testing and deployment should be a fundamental part of any edge AI development pipeline. Without a way to simultaneously run and test algorithms on hardware, it is difficult to foresee the memory, performance, and latency limitations that may arise when developing edge AI algorithms.

Some cloud-based model architectures are also not meant to run on any type of constrained or edge device, and predicting ahead of time can save firmware and ML teams months of pain.

IoT data does not equal big data

Big data refers to large data sets that can be analyzed to reveal patterns or trends. However, Internet of Things (IoT) data is not necessarily about quantity, but rather the quality of the data. Additionally, this data can be time-series sensor or audio data, or images, and may require preprocessing.

Combining traditional sensor data processing technologies such as digital signal processing (DSP) with AI/ML can produce new edge AI algorithms that provide accurate insights not possible with previous technologies. But IoT data is not big data, so the volume and analysis of these data sets used for edge AI development will vary. Rapidly experimenting with dataset size and quality based on the resulting model accuracy and performance is an important step on the path to production-deployable algorithms.

Developing hardware is hard enough

Building hardware is difficult without knowing whether the selected hardware can run edge AI software workloads. It’s critical to start benchmarking your hardware before choosing a bill of materials. With existing hardware, limitations of the memory available on the device may be more critical.

Even with early, small data sets, edge AI development platforms can start to provide performance and memory estimates for the types of hardware needed to run AI workloads.

Having a process for weighing device selection and benchmarking against early versions of edge AI models ensures hardware support is in place to support the required firmware and AI models that will run on the device.

Build, validate, and push new edge AI software to production

When choosing a development platform, it’s also worth considering the engineering support offered by different vendors. Edge AI encompasses data science, ML, firmware and hardware, and it's important for vendors to provide guidance in areas where internal development teams may need some additional support.

In some cases it is less about the actual model that will be developed and more about the planning of the system-level design process, including data infrastructure, ML development tools, testing, deployment environments and continuous integration, Continuous deployment (CI/CD) pipeline.

Finally, it’s important for edge AI development tools to accommodate the different users on your team—from ML engineers to firmware developers. The low-code/no-code user interface is a great way to quickly prototype and build new applications, while the API and SDK are useful for more experienced ML developers who can work better and faster using Python from Jupyter notebooks.

The platform provides the benefit of access flexibility, catering to the needs of multiple stakeholders or developers that may exist within cross-functional teams building edge AI applications.

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