The smallest computer in the world, an energy-efficient chip and other new IoT products

    Today we have prepared a digest with the latest “chip building” for IoT. We’ll talk about new devices for data encryption, the smallest computer in the world from IBM and the NVIDIA solution that simplifies the integration of deep learning systems into microprocessors.

    / photo Santi CC

    The smallest computer in the world by IBM

    At the IBM Think 2018 conference in March, the company introduced the smallest computer in the world. Its dimensions are 1x1 mm, which is even smaller than a grain of salt.

    The computer has a processor with several hundred thousand transistors, a RAM, a solar power system and a communication module with LEDs and a photo detector. By power, the microcomputer will be equal to a processor with x86 architecture of the 90s.

    IBM said that the new microchip will find application in blockchain technologies: it will serve as a data source for blockchain applications. For example, for logistics companies, such a solution will help to detect the actions of fraudsters in the supply chain.

    In this case, it becomes possible to track the origin of the goods. The low cost of manufacturing a chip (about 10 cents) will allow the mass integration of chips, for example, in electronic equipment so that customers can track where the goods came from and make sure of their quality. At IBM, they call their chip “crypto-anchor”, which protects data from theft and alteration.

    In addition, the microchip will be able to perform simple tasks for AI systems, for example, to classify the data provided.

    Release dates have not yet been named, but it is known that developers are already testing the first prototype. ZDNet claim that the microchip will appear on the market in a year and a half. TechCruch predicts the appearance of new items for 5 years.

    True Tele Random Number Generator from SK Telecom

    Scientists from the South Korean company SK Telecom have developed a microcomputer capable of generating truly random numbers. Such generators have already been created earlier and are even used in the work of cryptographic systems. However, the Korean company was the first to implement this idea in a 3x5x1 mm chip ( LxWxH).

    A tiny random number generator will be used in IoT devices to guarantee the protection of encrypted data while transferring it to other devices.

    The device uses the phenomenon of quantum shot noise (quantum shot noise). Chip LEDs emit photons that “bounce” from the internal walls of the device. They are captured by the built-in CMOS matrix , and the pulses generated by it are alreadyare passed to the randomness-extraction algorithm.

    SK Telecom and Nokia first demonstrated this technology in action last year. During the experiment, the SK Telecom server generated encryption keys and transmitted them to the Nokia 1830 fiber optic switch.

    The exact cost of the device is not known, however, Sean Kwak, head of the SK Telecom quantum technology laboratory, said it would be “a few dollars”.

    Energy efficient chip for IoT cryptosystems from MIT

    The Massachusetts Institute of Technology (MIT) has developed an energy-efficient microchip that consumes 400 times less energy than software implementations of public-key encryption. At the same time, the device works 500 times faster.

    Like most public-key cryptosystems, the chip uses elliptical cryptography techniques . At the same time, it is able to work with any elliptic curves, and its blocks, "responsible" for modular arithmetic, can process numbers up to 256 bits long (classical systems work with 16- or 32-bit values). Transport Layer Security Datagram Protocol - Datagram Transport Layer Security ( DTLS), which is responsible for processing encrypted data, is “wired” into the chip, which reduces the amount of memory required for its operation.

    Testing and specific plans for using the device in MIT have not yet been reported.

    / photo Fritzchens Fritz PD

    IoT Deep Learning: NVIDIA and Arm Joint Project

    In a collaboration announced by NVIDIA President Jensen Huang, the two companies decided to integrate the NVIDIA Deep Learning Accelerator (NVDLA) open architecture into the Arm Project Trillium platform for machine learning. The joint project is designed to facilitate and accelerate the implementation of deep learning systems in mobile and IoT devices.

    NVDLA is an accelerator for deep learning systems that has an open architecture and is built on the basis of the NVIDIA Xavier processor. NVDLA is based on powerful NVIDIA tools for developers (these are drivers, libraries, SDKs), among which new versions of the TensorRT programmable deep learning accelerator will soon appear.

    As for the Arm processor, it is specially “ sharpened»To work with machine learning systems. It performs more than 4.5 trillion operations per second (on mobile platforms), and this number can increase by 2–4 times during its “overclocking”.

    Together, these companies hope that these solutions will help chip makers and developers simplify the integration of AI systems into processors for IoT devices and provide the market with affordable products that support machine learning.

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