For decades, the basic architecture of remote control nodes has been composed of controllers, sensors, local storage, network connection interfaces, and batteries. This architecture is widely used in systems controlled by actual operations. In an industrial automation system, the controller monitors multiple sensors at different rates, saves the time-marked sensor data in the local or extended memory, and then transmits the data through industrial standard buses such as ProfiBus. ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? In the Advanced Driving Assistance System (ADAS) or Vehicle Event Recorder (EDR), multiple MCUs can simultaneously collect and control the data of the automotive electronic system, thereby providing a high-quality driving experience and trouble-free data protection. The medical system also has similar applications: key patient data obtained through sensors will be stored locally or uploaded regularly for centralized storage.
These systems are trying to solve the core and basic problems in the process of data collection, key data storage, and corresponding actions based on data analysis. However, different systems have different emphasis on solving problems. Industrial systems tend to collect massive amounts of data from many different sensors in a short time interval, and must keep detailed log records locally and remotely. The car system data generation rate is low, but the data importance is high. In some cases, the loss of data may threaten the lives of drivers and passengers. Most cars have a service life of more than ten years, so when choosing a memory, its service life and reliability have become very important considerations. The portable medical system pays attention to the performance of power consumption when choosing the ideal data storage. Since implantable medical devices, hearing aids and other devices are all driven by batteries, they are more inclined to choose memory with low energy consumption and high data storage accuracy. Failure-free data storage with long-term reliability and low energy consumption often becomes a major challenge for system designers to choose storage products.
With the gradual rise of the Internet of Things, all devices are beginning to be interconnected through the Internet. It is conservatively estimated that there will be 10 billion devices connected to the Internet in 2020, including new generation products such as automobiles, industrial automation equipment, implantable medical devices, wearable devices, and smart homes. Next-generation 5G networks have already begun to be deployed in certain regions and are expected to bear most of the traffic generated by the above-mentioned equipment. However, data scientists and system designers still have several unresolved problems:
Which devices need to be connected to the cloud? How much information needs to be transmitted? How much information can be processed locally? Who pays for the cloud?
One solution is to upload all the information to the cloud and process the information remotely. But this scheme is only suitable for small-scale and decentralized systems. With the continuous improvement of the world's interconnection, there will be a large number or even surplus of systems for uploading information. In this case, we need to consider the cost difference between network and local storage and processing. In the course of driving, an autonomous car will generate several G of data per hour. Therefore, in order to predict future demand, we must now decide which information needs to be transmitted in real time and which can be stored locally, so that it can be compressed and transmitted in the future. System designers in the industrial and medical fields face the same problem. In the process of "Industry 4.0", the method of "uploading all data to the cloud" is gradually changing to a method of "local processing and intelligent uploading". Therefore, how to choose the best local data storage is extremely important for future system development.
In order to save important data, these systems all need high-reliability, low-power, and high-security storage. One method is to use existing flash memory to record data. Flash memory technology has the ability to read efficiently, so it has been widely used for storage of boot code and firmware. For devices in existing systems, when performing write operations, designers can use flash memory to record data without understanding the technical limitations of flash memory. Flash memory cells can only store new data if they have been erased in advance. When programming the flash memory cell, the logic value can be changed from "1" to "0". In the next upgrade, if the storage unit needs to maintain the logical value "1", the data needs to be erased. In order to speed up the erase speed and shorten the program time, flash memory manufacturers have designed various page, block and sector architectures. A page is the smallest data storage unit that can be programmed into the flash memory at one time. The flash memory device has an internal page capacity buffer for temporary data storage. When the data transfer of the external interface is completed, the device will immediately execute the page program on the erased page in the main array. If the page contains old data, it must be erased before the program starts. Each time the erase is performed, the flash memory cell is degraded. This phenomenon is usually recorded quantitatively as an index of durability in flash memory. The most durable flash memory devices generally can withstand 100,000 cycles of erasing procedures, and storage stability cannot be guaranteed after reaching this limit. Although this number seems huge, it is difficult to meet even the needs of low-end data logging systems.
Some manufacturers use byte programming and push the programming from the buffer to the flash memory. Although this design can simplify the program running in the device, it cannot free the flash memory from potential endurance limitations. In order to offset the above limitations, system designers are forced to adopt a complex file system to ensure wear levelling of flash memory cells. The software of the file system will slow down the running speed of the system.
Imagine a situation where designers consider using a flash-based memory to record data. In industrial automation and asset management systems, sensor nodes will capture data several times per second, sample various sensors regularly, and then organize data packets and upload them to the network. Generally speaking, the number of samples of a data packet varies from 16 bytes to 128 bytes. Since the risk of power failure cannot be completely avoided, in order to prevent data loss, designers use non-volatile memory to store data. Vibration sensors or stepper motor position sensors send pulsed data every few milliseconds, while temperature or humidity sensors send data every few seconds, and data packets store data from many sensors.
The following table is a comparative analysis of data packet capacity and sampling rate, as well as the law of consumption of flash memory during data recording. Analyze 8M flash memory with 100,000 endurance cycles
The following chart provides a clear interpretation of this data. We found that for low-end systems that record 8-16 bytes of data every 1 millisecond, 8 megabytes of flash memory will wear out within 5 years. However, the wear and tear period of automobiles or industrial systems should exceed 10 years.
If the low-cost and high-risk method of simply adding flash memory is adopted, a complex file system is required to manage wear leveling. If the file system is not deployed, the system needs to periodically perform a chip erase cycle after replacing the entire memory. In today's Internet of Things world, with the continuous surge of data collection terminals, this problem is getting worse. Flash-based memory is very suitable for storing startup codes and firmware programs whose write cycles do not exceed 1000 times during the life of the product. An ideal way to solve this data recording problem is to use high-endurance and non-volatile memory, which will not cause data risks due to program and erasure delays. F-RAM has the durability to withstand up to 1014 erasing cycles, instantaneous non-volatility, and does not require programming and erasing operations, and stores all data entering the device interface in real time. For example, a 4M F-RAM storage can process a 128-byte data stream every 10 microseconds, and it will not be worn out in more than 1,000 years.
F-RAM memory cells consume power only when writing or reading, and the standby energy consumption is only a few microamperes, so F-RAM is the best solution for those relying on battery-driven products. F-RAM is suitable for hearing aids with high energy consumption requirements and high-end wearable medical devices for heart rate sampling. In addition, the data in the car system will continue to be recorded into the memory, and the flash-based system cannot capture the data during the flash memory "programming" period. Only data storage based on F-RAM can provide high reliability for the system.
F-RAM with almost unlimited endurance, ultra-low power consumption and instantaneous non-volatility is very suitable for important data storage in the interconnected world.
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