Climate change poses one of the most significant challenges to humanity. As a result of these climatic changes, the frequency of weather, climate, and water-related disasters has multiplied fivefold over the past 50 years, resulting in over 2 million deaths and losses exceeding $3.64 trillion USD. Leveraging AI-powered technologies for sustainable development and combating climate change is a promising avenue. Numerous significant publications are dedicated to using AI to improve renewable energy forecasting, enhance waste management, and monitor environmental changes in real time. However, very few research studies focus on making AI itself environmentally sustainable. This oversight regarding the sustainability of AI within the field might be attributed to a mindset gap and the absence of comprehensive energy datasets. In addition, with the ubiquity of edge AI systems and applications, especially on-device learning, there is a pressing need to measure, analyze, and optimize their environmental sustainability, such as energy efficiency. To this end, in this paper, we propose large-scale energy datasets for edge AI, named DeepEn2023, covering a wide range of kernels, state-of-the-art deep neural network models, and popular edge AI applications. We anticipate that DeepEn2023 will improve transparency in sustainability in on-device deep learning across a range of edge AI systems and applications.

AI solutions have already been implemented across industries, helping to increase efficiency, reduce costs, improve safety, and more. New advancements in edge AI processing are now enabling companies to take AI applications to the next level with multiple large, complex deep neural networks (DNNs). Analog compute-in-memory is a revolutionary new approach that is bringing incredible performance, power, and cost advantages to the edge AI industry. Here are predictions for three key markets that will be reshaped by this new approach to edge AI processing. AI is starting to completely transform the market for video security, which includes measures to deny unauthorized access and protect personnel/property.

In a study published by IBM in May, for example, 94 percent of surveyed executives said their organizations will implement edge computing in the next five years. From smart hospitals and cities to cashierless shops to self-driving cars, edge AI -- the combination of edge computing and AI -- is needed more than ever. Businesses have been slammed by logistical problems, worker shortages, inflation and uncertainty caused by the ongoing pandemic. Edge AI solutions can be used as a bridge between humans and machines, enabling improved forecasting, worker allocation, product design and logistics. While edge computing is rapidly becoming a must-have for many businesses, deployments remain in the early stages.

Many businesses have come to understand that Edge AI is not just an option but a necessity not just because of the various ways Edge AI applications are revolutionizing industries but because it is the inevitable future. Edge AI applications enable the processing of data on-site, the secure transfer of data, and increased privacy on Edge AI devices. Artificial intelligence algorithms can be run and processed without having to first send data to the cloud, thereby reducing the turnaround time for the generation of results.

Silicon Valley adaptive computing bellwether Xilinx announced its entrance into the growing system-on-module (SOM) market today, with a portfolio of palm-sized compute modules for embedded applications that accelerate AI, machine learning and vision at the edge. Xilinx Kria will eventually expand into a family of single board computers based on reconfigurable FPGA (Field Programmable Gate Array) technology, coupled to Arm core CPU engines and a full software stack with an app store, the first of which is specifically is targeted at AI machine vision and inference applications. The Xilinx Kria K26 SOM employs the company's UltraScale multi-processor system on a chip (MPSoC) architecture, which sports a quad-core Arm Cortex A53 CPU, along with over 250 thousand logic cells and an H.264/265 video compression / decompression engine (CODEC). This may sound like alphabet soup as I spit out acronyms, however, the underlying solution is a compelling offering for developers and engineers looking to give new intelligent systems, in industries like security, smart cities, retail analytics, autonomous machines and robotics, the ability to see, infer information and adapt to their deployments in the field. Also on board the Xilinx Kria K26 SOM is 4GB of DDR4 memory and 245 general purpose IO, along with the ability to support 15 cameras, up to 40 Gbps of combined Ethernet throughput, and four USB 2/3 compatible ports.

With the rise of AI at the edge comes a whole host of new requirements for memory systems. Can today's memory technologies live up to the stringent demands of this challenging new application, and what do emerging memory technologies promise for edge AI in the long-term? The first thing to realize is that there is no standard "edge AI" application; the edge in its broadest interpretation covers all AI-enabled electronic systems outside the cloud. That might include "near edge," which generally covers enterprise data centers and on-premise servers. Further out are applications like computer vision for autonomous driving.

On April 16, 2020, Intel and Udacity jointly announced their new Intel Edge AI for IoT Developers Nanodegree program to train the developer community in deep learning and computer vision. If you are wondering where AI is headed, now you know, it's headed to the edge. Edge computing is the concept of storing data and computing data directly at the location where it is needed. The global edge computing market is forecasted to reach 1.12 trillion dollars by 2023. Intel and Udacity aim to train 1 million developers.

Lattice Semiconductor announced availability of performance enhancements and new and improved application reference designs for its award-winning sensAI solutions stack. The performance enhancements include support for more compact/efficient neural network models and deeper quantization support to accommodate larger models for processing higher resolution and/or faster frames-per-second images in vision applications, delivering more accurate Edge AI performance. The new reference designs let sensAI customers quickly and easily create popular AI experiences, including key phrase detection and human face recognition. "MCUs struggle to deliver the performance needed for Edge AI while still maintaining strict power budgets. But thanks to their small size, support for parallel processing and sensor-agnostic AI inferencing, Lattice FPGAs are a compelling platform for any number of Edge AI applications requiring low power operation," said Hussein Osman, Market Segment Manager, Lattice Semiconductor.