{"id":6522,"date":"2023-10-17T21:05:22","date_gmt":"2023-10-17T21:05:22","guid":{"rendered":"https:\/\/dailyai.com\/?p=6522"},"modified":"2023-10-17T21:19:03","modified_gmt":"2023-10-17T21:19:03","slug":"new-wearables-technology-enables-local-machine-learning-processing","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2023\/10\/new-wearables-technology-enables-local-machine-learning-processing\/","title":{"rendered":"Une nouvelle technologie pour les appareils portables permet un traitement local de l'apprentissage automatique"},"content":{"rendered":"

A new era of smartwatches and wearable technology might just be around the corner, with the introduction of a new type of transistor capable of running AI algorithms.<\/b><\/p>\n

This reconfigurable transistor operates on a fraction of the electricity compared to its silicon-based counterparts. It could herald a new wave of smartwatches and wearables equipped with potent AI technology if implemented.\u00a0<\/span><\/p>\n

Currently, the energy demands of many AI algorithms make them unsuitable for traditional wearables, as they would quickly drain the battery.\u00a0<\/span><\/p>\n

To process data using machine learning algorithms, smartwatches, wearables, and other portable sensors have to send the data wirelessly to an AI system in the cloud, which then analyzes the data and sends it back to the device.\u00a0<\/span><\/p>\n

Local processing at the device level is considerably faster than this process, cutting data processing latency. Low latency is crucial for time-sensitive technologies such as manufacturing equipment and driverless vehicles.\u00a0<\/span><\/p>\n

This is also relevant to Internet of Things (IoT) systems, which use computers to process complex data locally to sensors rather than sending data to the cloud, also called edge computing.\u00a0<\/span><\/p>\n

As <\/span>Mark Hersam at Northwestern University in Illinois.explained<\/span><\/a>, \u201cEvery time data are passed around, it increases the likelihood of the data being stolen. If personal health data is processed locally \u2014 such as on your wrist in your watch \u2014 that presents a much lower security risk.\u201d<\/span><\/p>\n

These are some of the problems researchers at Northwestern University<\/a> are attempting to solve with their new lightweight transistors, which would be embedded into portable devices.<\/p>\n

Machine learning technologies for portable devices<\/span><\/h2>\n

The key differentiation of these new transistors is their composition of molybdenum disulfide and carbon nanotubes. <\/span><\/p>\n

These materials allow the transistor to be perpetually reconfigured by electric fields, handling multiple steps in AI-driven processes almost instantaneously.\u00a0<\/span><\/p>\n

In contrast, silicon-based transistors can only manage one step at a time, acting as minuscule on-or-off electronic switches. As a result, an AI task that would typically necessitate 100 silicon-based transistors might only require one of these reconfigurable transistors, leading to a drastic cut in energy use.<\/span><\/p>\n

\u201cThe low energy results from the fact that we can implement the [AI algorithm] with a 100-fold reduction in the number of transistors, compared to conventional silicon technology,\u201d stated Mark Hersam at Northwestern University in Illinois.<\/span><\/p>\n

Hersam and his research team showcased the prowess of these transistors by applying them to a standard machine-learning-based AI algorithm that analyzed heartbeat data from 10,000 electrocardiogram tests.\u00a0<\/span><\/p>\n

Impressively, the AI managed a 95% accuracy rate in categorizing the heartbeat data samples into one “normal” group and five distinct “arrhythmic” groups, including premature ventricular contraction.<\/span><\/p>\n

Vinod Sangwan, another research team member at Northwestern University, emphasized the potential implications of this advancement, especially for devices with low battery life or can’t maintain consistent internet connectivity for cloud-based AI processing.\u00a0<\/span><\/p>\n

However, incorporating these transistors into existing workflows while ensuring their durability is essential to commercial viability and remains challenging.\u00a0<\/span><\/p>\n

This is the latest entry in a line of breakthroughs that bring machine learning to low-power devices.<\/p>\n

Earlier in the year, researchers at IBM built lightweight brain-inspired chips<\/a> capable of processing algorithmic workloads with low power demands, again showing promise for portable devices.<\/p>\n

In time, these technologies could help power autonomous bio-inspired robots<\/a> that process data locally, similarly to organic organisms.<\/p>\n","protected":false},"excerpt":{"rendered":"

Une nouvelle \u00e8re de smartwatches et de technologies portables pourrait bien s'ouvrir avec l'introduction d'un nouveau type de transistor capable d'ex\u00e9cuter des algorithmes d'intelligence artificielle. Ce transistor reconfigurable fonctionne avec une fraction de l'\u00e9lectricit\u00e9 par rapport \u00e0 ses homologues \u00e0 base de silicium. S'il est mis en \u0153uvre, il pourrait annoncer une nouvelle vague de smartwatches et de wearables \u00e9quip\u00e9s d'une puissante technologie d'IA. Actuellement, les besoins en \u00e9nergie de nombreux algorithmes d'intelligence artificielle les rendent inadapt\u00e9s aux dispositifs portables traditionnels, car ils \u00e9puiseraient rapidement la batterie. Pour traiter les donn\u00e9es \u00e0 l'aide d'algorithmes d'apprentissage automatique, les smartwatches, les wearables et autres capteurs portables doivent envoyer les donn\u00e9es sans fil<\/p>","protected":false},"author":2,"featured_media":6523,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[440,439,105,100],"class_list":["post-6522","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-engineering","tag-hardware","tag-machine-learning","tag-semiconductors"],"yoast_head":"\nNew wearables technology enables local machine learning processing | DailyAI<\/title>\n<meta name=\"description\" content=\"A new era of smartwatches and wearable technology might just be around the corner, with the introduction of a new type of transistor capable of running AI algorithms.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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