{"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\/pt\/2023\/10\/new-wearables-technology-enables-local-machine-learning-processing\/","title":{"rendered":"A nova tecnologia para dispositivos port\u00e1teis permite o processamento local da aprendizagem autom\u00e1tica"},"content":{"rendered":"<p><b>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<p><span style=\"font-weight: 400;\">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<p><span style=\"font-weight: 400;\">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<p><span style=\"font-weight: 400;\">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<p><span style=\"font-weight: 400;\">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<p><span style=\"font-weight: 400;\">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<p><span style=\"font-weight: 400;\">As <\/span><a href=\"https:\/\/interestingengineering.com\/innovation\/novel-design-makes-ai-100x-energy-efficient\"><span style=\"font-weight: 400;\">Mark Hersam at Northwestern University in Illinois.explained<\/span><\/a><span style=\"font-weight: 400;\">, \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<p>These are some of the problems <a href=\"https:\/\/news.northwestern.edu\/stories\/2023\/10\/ai-just-got-100-fold-more-energy-efficient\/\">researchers at Northwestern University<\/a> are attempting to solve with their new lightweight transistors, which would be embedded into portable devices.<\/p>\n<h2><span style=\"font-weight: 400;\">Machine learning technologies for portable devices<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The key differentiation of these new transistors is their composition of molybdenum disulfide and carbon nanotubes. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">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<p><span style=\"font-weight: 400;\">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<p><span style=\"font-weight: 400;\">\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<p><span style=\"font-weight: 400;\">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<p><span style=\"font-weight: 400;\">Impressively, the AI managed a 95% accuracy rate in categorizing the heartbeat data samples into one &#8220;normal&#8221; group and five distinct &#8220;arrhythmic&#8221; groups, including premature ventricular contraction.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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&#8217;t maintain consistent internet connectivity for cloud-based AI processing.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, incorporating these transistors into existing workflows while ensuring their durability is essential to commercial viability and remains challenging.\u00a0<\/span><\/p>\n<p>This is the latest entry in a line of breakthroughs that bring machine learning to low-power devices.<\/p>\n<p>Earlier in the year, researchers at <a href=\"https:\/\/dailyai.com\/2023\/08\/ibm-announces-brain-like-ai-chip-with-lower-power-demands\/\">IBM built lightweight brain-inspired chips<\/a> capable of processing algorithmic workloads with low power demands, again showing promise for portable devices.<\/p>\n<p>In time, these technologies could help power autonomous <a href=\"https:\/\/dailyai.com\/2023\/08\/the-evolution-of-bio-inspired-ai-developments-and-future-direction\/\">bio-inspired robots<\/a> that process data locally, similarly to organic organisms.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A introdu\u00e7\u00e3o de um novo tipo de trans\u00edstor capaz de executar algoritmos de intelig\u00eancia artificial pode estar ao virar da esquina. Este trans\u00edstor reconfigur\u00e1vel funciona com uma fra\u00e7\u00e3o de eletricidade em compara\u00e7\u00e3o com os seus equivalentes baseados em sil\u00edcio. Se for implementado, poder\u00e1 anunciar uma nova vaga de smartwatches e de dispositivos port\u00e1teis equipados com uma potente tecnologia de IA.  Atualmente, as necessidades energ\u00e9ticas de muitos algoritmos de IA tornam-nos inadequados para os wearables tradicionais, uma vez que esgotam rapidamente a bateria.  Para processar dados utilizando algoritmos de aprendizagem autom\u00e1tica, os smartwatches, os wearables e outros sensores port\u00e1teis t\u00eam de enviar os dados sem fios<\/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":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>New 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 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