Technological Advances that are Driving Edge Computing Adoption
The evolution of a technology as a pervasive force is often a time-consuming process. But edge computing is different — its impact radius is increasing at an exponential rate. AI is an area where edge is playing a crucial role, and it is evident from how companies like Kneron, IBM, Synaptic, Run:ai, and others are investing in the tech.
In other industries, such as space-tech or healthcare, companies including Fortifyedge and Sidus Space are planning big for edge computing.
However, such a near-ubiquitous presence is bound to trigger questions regarding app performance and security. Edge computing is no exception, and in recent years, it has become more inclusive in terms of accommodating new tools.
In my experience as the Head of Emerging Technologies for startups, I have found that understanding where edge computing is headed before you adopt it – is imperative. In my previous article for ReadWrtie — I discussed major enablers in edge computing. In this article, my focus is on recent technical developments that are trying to solve pressing industrial concerns and shape the future.
JavaScript-based AI/ML libraries are popular and mature for web-based applications. The driving force is increased efficacy in delivering personalized content by running edge analytics. But it has constraints and does not provide security like a sandbox. The VM module does not guarantee secured sandboxed execution. Besides, for container-based applications, startup latency is the prime constraint.
WebAssembly is emerging fast as an alternative for edge application development. It is portable and provides security with a sandbox runtime environment. As a plus, it allows faster startup for containers than cold (slow) starting containers.
Businesses can leverage WebAssembly-based code for running AI/ML inferencing in browsers as well as program logic over CDN PoPs. Its permeation across industries has grown significantly, and research studies support it by analyzing binaries from several sources ranging from source code repositories, package managers, and live websites. Use cases that recognize facial expressions and process images or videos to improve operational efficacy will benefit more from WebAssembly.
Edge AI refers to the deployment of AI/ML applications at the edge. However, most edge devices are not as resource-rich as cloud or server machines in terms of computing, storage, and network bandwidth.
TinyML is the use of AI/ML on resource-constraint devices. It drives the edge AI implementation at the device edge. Under TinyML, the possible optimization approaches are optimizing AI/ML models and optimizing AI/ML frameworks, and for that, the ARM architecture is a perfect choice.
It is a widely accepted architecture for edge devices. Research studies show that for workloads like AI/ML inferencing, the ARM architecture has a better price per performance as compared to x86.
For model optimization, developers use model pruning, model shrinking, or parameter quantization.
But TinyML comes with a few boundaries in terms of model deployment, maintaining different model versions, application observability, monitoring, etc. Collectively, these operational challenges are called TinyMLOPs.
