A 47.14-$\mu\text{W}$  200-MHz MOS/MTJ-Hybrid Nonvolatile Microcontroller Unit Embedding STT-MRAM and FPGA for IoT Applications

Natsui, Masanori; Suzuki, Daisuke; Tamakoshi, Akira; Watanabe, Takahito; Honjo, Haruo; Koike, Hideaki; Nasuno, T.; Ma, Yi; Tanigawa, T.; Noguchi, Y.; Yasuhira, M.; Sato, Hideo; Ikeda, Shoji; Ohno, Hideo; Endoh, Tetsuo; Hanyu, Takahiro

doi:10.1109/jssc.2019.2930910

Cited by 46 publications

(30 citation statements)

References 24 publications

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“…In a recent study, M. Natsu et al presented a fabricated MOS/MTJ-hybrid nonvolatile microcontroller with an embedding STT-MRAM dedicated to IoT applications [43]. The manufactured chip achieved ultra-low power consumption and high-speed operation, essential for sensors powered by harvested energy.…”

Section: Related Workmentioning

confidence: 99%

Design Considerations of a Nonvolatile Accumulator Based 8-bit Processor

Cagliari

Butzen²,

Brum³

2021

JICS

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The rise of the Internet of Things (IoT) and the constant growth of portable electronics have leveraged the concern with energy consumption due to the use of batteries in these devices. The nonvolatile memory (NVM) emerged as a solution to mitigate the problem due to its ability to retain data on sleep mode without a power supply. Nonvolatile processors (NVPs), in turn, may further improve energy saving, by making use of nonvolatile flip-flops (NVFFs) that store system state in parallel, allowing the device to be turned off when idle and resume execution instantly after power-on. This work describes the initial steps to implement a nonvolatile version of Neander, a hypothetical processor created for educational purposes. First, we implemented Neander in Register Transfer Level (RTL), separating the combinational logic from the sequential elements. Then, the latter were replaced by transistor-level descriptions ofvolatile flip-flops. We then validated this implementation by employing a mixed-signal simulation over a set of benchmarks. Results shown the expected behavior for the whole instruction set. Then, we implemented MTJ-based non-volatile flip-flops in circuit-level, using an open-source MTJ model. These elements were exhaustive validated using electrical simulations. With these results, we intend to carry on the implementation and fully equip our processor with nonvolatile features such as instant wake up.

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Section: Related Workmentioning

confidence: 99%

Design Considerations of a Nonvolatile Accumulator Based 8-bit Processor

Cagliari

Butzen²,

Brum³

2021

JICS

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“…[ 127 ] Instead, a variety of sensors and modules can be equipped in a self‐powered smart watch for health monitoring and communication. [ 128 ] Typically, a smart watch equipped with a microcontroller unit (47 μW), [ 129 ] a pedometer (75 μW), a heart rate sensor (105 μW), [ 130 ] a temperature sensor (30 μW), [ 131 ] and a wireless transmitter (40 μW) [ 132 ] only requires a total power supply of 297 μW, which could be fully powered by the energy harvesters. [ 108,111 ] Apart from powering the smart watches with versatile functions, the limb swing excited energy harvester can also be used for human activity recognition.…”

Section: Possible Self‐powered Applicationsmentioning

confidence: 99%

Recent Advances in Human Motion Excited Energy Harvesting Systems for Wearables

et al. 2020

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The development of wearable electronics and sensors is constrained by the limited capacity of their batteries. Therefore, energy harvesting from human motion is explored to provide a promising embedded sustainable power supply for wearable devices. Herein, the principles, development, and applications of human motion excited energy harvesters are reviewed. The energy harvesters are classified based on the human motions with distinguished characteristics: center of mass motion (COM), joint motion, foot strike, and limb swing motion. According to the motion characteristics, the principles, features, and limitations of different energy harvesters are discussed, and the power generation performances are compared. Finally, various self-powered applications enabled by embedded energy harvesters are introduced, so as to show the great potential of embedded energy harvesting systems in boosting the development of the wearables.

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“…A great deal of complementary metaloxide-semiconductor (CMOS) based accelerators have been proposed such as Origami [32], Eyeriss [33], iFPNA [34] etc. Moreover, with the development of magnetic tunnel junction (MTJ) technology, the hybrid CMOS/MTJ based logic circuit can significantly decrease the power consumption of brainware processor [8], [35], [36]. The CMOS or hybrid CMOS/MTJ based brainware processors will play a major role in object detection.…”

Section: B Hardware Acceleration Of Yolomentioning

confidence: 99%

A Systematic Study of Tiny YOLO3 Inference: Toward Compact Brainware Processor With Less Memory and Logic Gate

Endoh

2020

IEEE Access

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The emerging of deep neural networks, especially the convolutional neural network (CNN), substantially promotes the fast development of brainware processors in object detection. However, the vast network architecture brings severe challenges to the design of brainware processor, which requires a large number of logic gates and memories. Therefore, a compact brainware processor with less memory and logic gate has a high demand in object detection. Typically, the object detection involves single-shot and multishot detectors in accordance with different detection principle. In the early stage, the multi-shot detector has a leading role in solving object detection issues, such as region-based convolutional neural networks (R-CNNs), faster R-CNNs etc. However, the multi-shot detector suffers from a low detection rate comparing with the single-shot detector. The you only look once (YOLO) algorithm, as the state-of-the-art real-time object detection algorithm, receives extensive attention from the academics and industry. Particularly, the lightweight YOLO algorithm, tiny YOLO3, has excellent potential for circuit design of compact brainware processor. Nonetheless, systematic studies of tiny YOLO3 are still missing up to the present. This paper offers a thorough review of the tiny YOLO3 algorithm, which can fill the gap in the field of object detection. Furthermore, the open solutions of compressing the tiny YOLO3 algorithm are proposed from the aspects of algorithm, hardware and emerging technology. The comprehensive study presented in this paper can not only enhance understanding of the tiny YOLO3 algorithm for researchers or engineers but also make a significant contribution to accelerating the development of compact brainware processor. INDEX TERMSTiny YOLO3, brainware processor, deep neural network, CNN, hardware acceleration VOLUME 4, 2016 II. RELATED WORK A. YOLO DEVELOPMENT J. Redmon firstly proposed the YOLO algorithm in May 2016 [22], and it has been evolved to four generations within four years. The base YOLO, motivated by fast R-CNNs, introduces the region-based concept to the neural network.Peculiarly, the input image is divided into different grid cells where two bounding boxes are predicted. In each bounding box, the center coordinate of object, confidence scores and the class probabilities are predicted. The confidence score is responsible for checking whether or not the object exists in the bounding box. The base YOLO has a good advantage in the perspective of speed and acceptable accuracy, but the following drawbacks retard the development of base YOLO:• The base YOLO is hard to handle the situation that the distance of two objects is very close. The detector may

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A 47.14-$\mu\text{W}$ 200-MHz MOS/MTJ-Hybrid Nonvolatile Microcontroller Unit Embedding STT-MRAM and FPGA for IoT Applications

Cited by 46 publications

References 24 publications

Design Considerations of a Nonvolatile Accumulator Based 8-bit Processor

Design Considerations of a Nonvolatile Accumulator Based 8-bit Processor

Recent Advances in Human Motion Excited Energy Harvesting Systems for Wearables

A Systematic Study of Tiny YOLO3 Inference: Toward Compact Brainware Processor With Less Memory and Logic Gate

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