領(lǐng)先全球Renesas quartz crystal oscillator專用智能家居，隨著傳感器和MCU成本的下降和出貨量的飆升，越來越多的組織試圖通過將傳感器驅(qū)動(dòng)的嵌入式AI添加到他們的產(chǎn)品中來加以利用。

汽車正在推動(dòng)這一趨勢(shì)——目前平均每輛非自動(dòng)駕駛汽車有100個(gè)傳感器，向30-50個(gè)微控制器發(fā)送信息，這些微控制器運(yùn)行大約100萬行代碼，每天每輛汽車產(chǎn)生1TB的數(shù)據(jù)。豪華汽車的數(shù)量可能是這個(gè)數(shù)字的兩倍，而自動(dòng)駕駛汽車增加傳感器檢查的幅度要大得多。

然而，這不僅僅是汽車行業(yè)的趨勢(shì)。隨著旋轉(zhuǎn)、往復(fù)和其他類型設(shè)備的創(chuàng)造者爭(zhēng)相增加狀態(tài)監(jiān)控和預(yù)測(cè)支持的有用性，以及大量新的消費(fèi)產(chǎn)品(從牙刷到真空吸塵器，再到健身監(jiān)控器)增加儀器和“智能”，工業(yè)設(shè)備正變得越來越“聰明”。因此，需要更加優(yōu)質(zhì)的有源晶體振蕩器元器件加以搭配使用。

每個(gè)月都會(huì)推出越來越多的智能設(shè)備。我們現(xiàn)在正處于一個(gè)點(diǎn)上，人工智能和機(jī)器學(xué)習(xí)在其異常重要的結(jié)構(gòu)中發(fā)現(xiàn)了進(jìn)入嵌入式設(shè)備核心的方法。例如，智能家居照明系統(tǒng)會(huì)根據(jù)房間內(nèi)是否有人而自動(dòng)開關(guān)。從各方面來看，這個(gè)系統(tǒng)看起來并不時(shí)尚。然而，當(dāng)你考慮所有的事情時(shí)，你會(huì)明白這個(gè)系統(tǒng)實(shí)際上是獨(dú)自決定選擇的。鑒于傳感器的貢獻(xiàn)，微控制器/片上系統(tǒng)(SoC)決定是否開燈。

要同時(shí)做到這一切，在邊緣、重要的限制范圍內(nèi)，擊敗多樣性，實(shí)現(xiàn)實(shí)時(shí)、麻煩的檢測(cè)，一點(diǎn)也不簡(jiǎn)單。在任何情況下，利用當(dāng)前的工具，整合信號(hào)機(jī)器學(xué)習(xí)的新選項(xiàng)(如Reality AI)變得越來越簡(jiǎn)單。

在許多不同的領(lǐng)域，機(jī)器學(xué)習(xí)和嵌入式系統(tǒng)的融合將帶來巨大的機(jī)遇。例如，醫(yī)療保健現(xiàn)在正在獲得將資源投入人工智能技術(shù)的回報(bào)。物聯(lián)網(wǎng)也將從人工智能的引入中獲得巨大收益。我們將擁有智能自動(dòng)化有源晶振解決方案，促進(jìn)能源節(jié)約、成本效率以及人類失誤的終結(jié)。

預(yù)測(cè)是許多ML/AI對(duì)話的中心，因?yàn)榻M織希望使用神經(jīng)網(wǎng)絡(luò)和深度學(xué)習(xí)來推測(cè)時(shí)間序列數(shù)據(jù)。價(jià)值在于吸收信息并迅速了解信息如何改變長期前景的能力。此外，很大一部分情況依賴于全球供應(yīng)鏈，這使得改進(jìn)變得更加難以精確預(yù)測(cè)。領(lǐng)先全球Renesas quartz crystal oscillator專用智能家居.

也許生產(chǎn)線上最不安全的位置現(xiàn)在是由機(jī)器處理的。由于嵌入式電子和工業(yè)自動(dòng)化的進(jìn)步，我們的OSC晶振有了突破性的微控制器來運(yùn)行裝配廠的整個(gè)機(jī)械生產(chǎn)系統(tǒng)。然而，這些機(jī)器中的大多數(shù)并不完全是自動(dòng)的，仍然需要一種人工干預(yù)。無論如何，機(jī)器學(xué)習(xí)的引入將有助于工程師制造真正智能的機(jī)器，這些機(jī)器可以在沒有人類干預(yù)的情況下工作。

Throughout the most recent years, as sensor and MCU costs dove and shipped volumes have gone through the roof, an ever-increasing number of organizations have attempted to exploit by adding sensor-driven embedded AI to their products.

Automotive is driving the trend– the average non-autonomous vehicle presently has 100 sensors, sending information to 30-50 microcontrollers that run about 1m lines of code and create 1TB of data per vehicle every day. Extravagance vehicles may have twice the same number of, and autonomous vehicles increase the sensor check significantly more drastically.

Yet, it's not simply an automotive trend. Industrial equipment is turning out to be progressively "brilliant" as creators of rotating, reciprocating and other types of equipment rush to add usefulness for condition monitoring and predictive support, and a huge number of new consumer products from toothbrushes, to vacuum cleaners, to fitness monitors add instrumentation and "smarts".

An ever-increasing number of smart devices are being introduced each month. We are now at a point where artificial intelligence and machine learning in its exceptionally essential structure has discovered its way into the core of embedded devices. For example, smart home lighting systems that automatically turn on and off depend on whether anybody is available in the room. By all accounts, the system doesn't look excessively stylish. Yet, when you consider everything, you understand that the system is really settling on choices all alone. In view of the contribution from the sensor, the microcontroller/system-on-chip (SoC) concludes if to turn on the light or not.

To do all of this simultaneously, defeating variety to achieve troublesome detections in real-time, at the edge, inside the vital limitations isn't at all simple. In any case, with current tools, integrating new options for machine learning for signals (like Reality AI) it is getting simpler.

They can regularly achieve detections that escape traditional engineering models. They do this by making significantly more productive and compelling utilization of data to conquer variation. Where traditional engineering approaches will ordinarily be founded on a physical model, utilizing data to appraise parameters, machine learning approaches can adapt autonomously of those models. They figure out how to recognize signatures straightforwardly from the raw information and utilize the mechanics of machine learning (mathematics) to isolate targets from non-targets without depending on physical science.

There are a lot of different regions where the convergence of machine learning and embedded systems will prompt great opportunities. Healthcare, for example, is now receiving the rewards of putting resources into AI technology. The Internet of Things or IoT will likewise profit enormously from the introduction of artificial intelligence. We will have smart automation solutions that will prompt energy savings, cost proficiency as well as the end of human blunder.

Forecasting is at the center of so many ML/AI conversations as organizations hope to use neural networks and deep learning to conjecture time series data. The worth is the capacity to ingest information and quickly acknowledge insight into how it changes the long-term outlook. Further, a large part of the circumstance relies upon the global supply chain, which makes improvements significantly harder to precisely project.

Probably the most unsafe positions in production lines are as of now being dealt by machines. Because of the advancement in embedded electronics and industrial automation, we have ground-breaking microcontrollers running the whole mechanical production systems in assembling plants. However, the majority of these machines are not exactly completely automatic and still require a type of human intercession. In any case, the time will come when the introduction of machine learning will help engineers concoct truly intelligent machines that can work with zero human mediation.