Due to its increasing availability, artificial intelligence has encouraged some decision-makers to combine it with existing technologies. Contextual AI for edge intelligence devices represents a compelling example. It can interpret and react to information by analyzing relevant aspects such as historical details and environmental parameters to provide more accurate, faster responses.

AI is creating demand for faster, smarter, and more efficient computing. Yet, with vast amounts of data generated every second, sending everything to the cloud for processing is impractical. This is where AI accelerators in edge computing become indispensable.

Edge computing is a crucial step forward for data analytics. Processing information on a distributed network of nearby endpoints raises local computing power and reduces latency from remote data centers.

At the same time, so much sensitive information moving between devices can raise security concerns. Zero-knowledge cryptography has emerged as a promising solution to that issue.

While keeping yourself informed about relevant engineering developments, you have probably heard more about artificial intelligence chips that work at the edge. These advanced components enable an ongoing interest to shorten the processing times for the world’s connected devices.

Transistors are the lifeblood of modern technology, fueling data transmission and storage worldwide. Experts have continuously refined these mechanisms, and they are essential components in various industries, including aerospace, defense and transportation.

Recent studies demonstrate how engineers might be entering a new age of transistors with memory-retention capabilities.

Internet of Things (IoT) deployment can be challenging for today’s manufacturers and supply chain organizations. Connectivity is becoming increasingly crucial to remaining competitive, but many businesses lack the tools and talent to implement new technology safely and effectively. New software platforms, including one from congatec called aReady.IOT, may provide an answer.

If you have dealt with large language models — specifically autoregressive decoder algorithms — you know the inconvenience of sluggish processing. Novel findings from KAIST and Google researchers on blockwise parallel decoding may offer a solution.