What started as a simple home repair project ended with multiple trips to the hardware store, cursing in the aisles, and a vow to never buy from a specific manufacturer ever again. A single defective bolt, which had evaded quality inspection and been packaged, shipped, and unfortunately purchased by me. The product packaging, installation instructions, and final functionality were all exemplary. But a single defective bolt, which costs only pennies in the product's bill of materials, was enough to sour me on the whole experience. Manufacturers and brand owners are under tremendous pressure to ensure premium end-to-end product quality, especially as consumers increasingly demand perfection.

While IoT at the edge of the network continues to make strides, resource constraints pose ample challenges to these devices. This can enable a variety of tasks, from autonomous driving to real-time video streaming to preventative maintenance of equipment. Processing at the edge circumvents the time delays and data security challenges of centralized computing: Instead of sending data back and forth to a data center or a cloud, data is processed locally. Companies are beginning to reap the benefits of edge processing in ways they barely imagined five years ago. Consider retailers, which now use edge processing for video surveillance at the register -- not only to minimize product loss but also to target other customer services issues in checkout.

If edge computing capabilities can be woven into the very fabric of our cities, this might come to revolutionize the way we interact with them. Over the past decade, many cities have launched huge (and hugely expensive) "smart city" initiatives. In the early years of this shift, the focus was on the collection and centralization of data. However, in recent years a new model has emerged – de-centralized data processing performed "at the edge." As edge computing has become more common, data engineers working for city governments and industry have become more adept at equipping existing systems with IoT and edge processing functionality, and at using AI and ML to extract actionable insights.

Many vendors like Cisco and Intel are proponents of Edge Processing (also called Edge computing). The main idea behind Edge Computing is to push processing away from the core and towards the Edge of the network. For IoT, that means pushing processing towards the sensors or a gateway. This enables data to be initially processed at the Edge device possibly enabling smaller datasets sent to the core. Devices at the Edge may not be continuously connected to the network.

Many vendors like Cisco and Intel are proponents of Edge Processing (also called Edge computing). The main idea behind Edge Computing is to push processing away from the core and towards the Edge of the network. For IoT, that means pushing processing towards the sensors or a gateway. This enables data to be initially processed at the Edge device possibly enabling smaller datasets sent to the core. Devices at the Edge may not be continuously connected to the network.

Artificial intelligence (AI) processing today is mostly done in a cloud-based data center. The majority of AI processing is dominated by training of deep learning models, which requires heavy compute capacity. In the last 6 years, the industry has experienced a 300,000X growth in compute requirements, with graphics processing units (GPUs) providing most of that horsepower. According to a new report from Tractica, however, as the diversity of AI applications grows, an increasing amount of AI processing will be handled within edge devices rather than in a centralized, cloud-based environment. Tractica forecasts that AI edge device shipments will increase from 161.4 million units in 2018 to 2.6 billion units worldwide annually by 2025.

NXP Semiconductors N.V. (NASDAQ:NXPI) announced a comprehensive, easy-to-use machine learning (ML) environment for building innovative applications with cutting-edge capabilities. Customers can now easily implement ML functionality on NXP's breadth of devices from low-cost microcontrollers (MCUs) to breakthrough crossover i.MX RT processors and high-performance application processors. The ML environment provides turnkey enablement for choosing the optimum execution engine from among Arm Cortex cores to high-performance GPU/DSP (Graphics Processing Unit/Digital Signal Processor) complexes and tools for deploying machine learning models, including neural nets, on those engines. Embedded Artificial Intelligence (AI) is quickly becoming an essential capability for edge processing, gives'smart' devices an ability to become'aware' of its surroundings and make decisions on the input received with little or no human intervention. NXP's ML environment enables fast-growing machine learning use-cases in vision, voice, and anomaly detections.

Embedded Artificial Intelligence (AI) is quickly becoming an essential capability for edge processing, gives'smart' devices an ability to become'aware' of its surroundings and make decisions on the input received with little or no human intervention. NXP's ML environment enables fast-growing machine learning use-cases in vision, voice, and anomaly detections. The vision-based ML applications utilize cameras as inputs to the various machine learning algorithms of which neural networks are the most popular. These applications span most market segments and perform functions such as object recognition, identification, people-counting and others. Voice Activated Devices (VADs) are driving the need for machine learning at the edge for wake word detection, natural language processing, and for'voice as the user-interface' applications.

The huge coverage devoted to the topics of AI and edge computing sparked an idea when I recently visited JFK Airport. My journey coincided with a severe weather storm that disrupted travel along the East Coast. This situation illustrates how customer service agents assist passengers (at the edge) when dealing with uncertainty and changing circumstances (relying predictive analysis and intelligent decision-making under uncertainty). The IoT is imminent – and so are the security challenges it will inevitably bring. Get up to speed on IoT security basics and learn how to devise your own IoT security strategy in our new e-guide.

Many vendors like Cisco and Intel are proponents of Edge Processing (also called Edge computing). The main idea behind Edge Computing is to push processing away from the core and towards the Edge of the network. For IoT, that means pushing processing towards the sensors or a gateway. This enables data to be initially processed at the Edge device possibly enabling smaller datasets sent to the core. Devices at the Edge may not be continuously connected to the network.