One of the characteristic features of the next-generation of Industry 4.0 is human-centricity, which in turn includes two technological advancements: Artificial Intelligence and the Industrial Metaverse. In this work, we assess the impact that AI played on the advancement of three technologies that emerged to be cornerstones in the fourth generation of industry: intelligent industrial robotics, unmanned aerial vehicles, and additive manufacturing. Despite the significant improvement that AI and the industrial metaverse can offer, the incorporation of many AI-enabled and Metaverse-based technologies remains under the expectations. Safety continues to be a strong factor that limits the expansion of intelligent industrial robotics and drones, whilst Cybersecurity is effectively a major limiting factor for the advance of the industrial metaverse and the integration of blockchains. However, most research works agree that the lack of the skilled workforce will no-arguably be the decisive factor that limits the incorporation of these technologies in industry. Therefore, long-term planning and training programs are needed to counter the upcoming shortage in the skilled workforce.
Powering next-generation industry 4.0 by a self-learning and low-power neuromorphic system
With the continuous development of technologies, our society is approaching the next stage of industrialization. The Fourth Industrial Revolution also referred to as Industry 4.0, redefines the manufacturing system as a smart and connected machinery system with fully autonomous operation capability. Several advanced cutting-edge technologies, such as cyber-physical systems (CPS), the internet of things (IoT), and artificial intelligence, are believed to the essential components to realize Industry 4.0. In this paper, we focus on a comprehensive review of how artificial intelligence benefits Industry 4.0, including potential challenges and possible solutions. A panoramic introduction of neuromorphic computing is provided, which is one of the most promising and attractive research directions in artificial intelligence. Subsequently, we introduce the vista of the neuromorphic-powered Industry 4.0 system and survey a few research activities on applications of artificial neural networks for IoT.
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- NSF-PAR ID:
- 10209058
- Date Published:
- Journal Name:
- ACM International Conference on Nanoscale Computing and Communication
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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CIEC panel (Ed.)Abstract: When starting small businesses, particularly in high-tech sectors like artificial intelligence (AI), digital twins, or the Internet of Things (IoT), women and underrepresented minority groups face additional hurdles in securing funding and investment. Not only is such a discrepancy in investment socially unjust, but it deprives the US of the advantages in innovation and global competition that could stem from the widening participation of the underrepresented population in innovative sectors. Although targeted support to women and underrepresented minority-owned businesses is being provided by the federal government and the private sector, more remains to be done to close the investment gap. The US Small Business Administration (SBA, 2013) provides more than $3.5 billion in funding to over 5,000 startups per year through its Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs. Moreover, the Small Business Act provides these programs with a mandate to target women and underrepresented minority groups. Despite this mandate from SBA, only 15% of those funds went to minority-owned companies (SBA, 2013). Funding opportunities from the private sector tell a similar story. Diversity VC, a non-profit partnership promoting diversity in Venture Capital, reported in 2019 that in a comprehensive survey (Azevedo, 2019) of around 10,000 founders receiving venture capital backing, only 9% were women and a mere 1% were Black. In order to i) accelerate innovation and increase participation of under-represented minorities in start-ups of “new industry”, and ii) to ensure US competitiveness in the global market, in 2010, the National Science Foundation (NSF) introduced the Small Business Postdoctoral Research Diversity Fellowship (SBPRDF) program and selected the American Society of Engineering Education (ASEE) to administer the program. In recognition of ASEE’s successful performance in meeting the objectives of the SBPRDF program, in 2019 NSF/IIP (Industrial Innovation and Partnerships) program leadership selected ASEE to administer the Innovative Postdoctoral Entrepreneurial Research Fellowship (IPERF) program. The overarching goal of the IPERF program is to emphasize and strengthen the entrepreneurial development of underrepresented Fellows. The IPERF program also aims to advance best practices in postdoctoral programs and impart cross-disciplinary expertise in the application of new technologies like AI and IoT in “new industries” based on bioengineering and biochemistry technologies.more » « less
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Abstract In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.
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The traditional von Neumann architecture limits the increase in computing efficiency and results in massive power consumption in modern computers due to the separation of storage and processing units. The novel neuromorphic computation system, an in-memory computing architecture with low power consumption, is aimed to break the bottleneck and meet the needs of the next generation of artificial intelligence (AI) systems. Thus, it is urgent to find a memory technology to implement the neuromorphic computing nanosystem. Nowadays, the silicon-based flash memory dominates non-volatile memory market, however, it is facing challenging issues to achieve the requirements of future data storage device development due to the drawbacks, such as scaling issue, relatively slow operation speed, and high voltage for program/erase operations. The emerging resistive random-access memory (RRAM) has prompted extensive research as its simple two-terminal structure, including top electrode (TE) layer, bottom electrode (BE) layer, and an intermediate resistive switching (RS) layer. It can utilize a temporary and reversible dielectric breakdown to cause the RS phenomenon between the high resistance state (HRS) and the low resistance state (LRS). RRAM is expected to outperform conventional memory device with the advantages, notably its low-voltage operation, short programming time, great cyclic stability, and good scalability. Among the materials for RS layer, indium gallium zinc oxide (IGZO) has shown attractive prospects in abundance and high atomic diffusion property of oxygen atoms, transparency. Additionally, its electrical properties can be easily modulated by controlling the stoichiometric ratio of indium and gallium as well as oxygen potential in the sputter gas. Moreover, since the IGZO can be applied to both the thin-film transistor (TFT) channel and RS layer, it has a great potential for fully integrated transparent electronics application. In this work, we proposed amorphous transparent IGZO-based RRAMs and investigated switching behaviors of the memory cells prepared with different top electrodes. First, ITO was choosing to serve as both TE and BE to achieve high transmittance. A multi-target magnetron sputtering system was employed to deposit all three layers (TE, RS, BE layers) on glass substrate. I-V characteristics were evaluated by a semiconductor parameter analyzer, and the bipolar RS feature of our RRAM devices was demonstrated by typical butterfly curves. The optical transmission analysis was carried out via a UV-Vis spectrometer and the average transmittance was around 80% out of entire devices in the visible-light wavelength range, implying high transparency. We adjusted the oxygen partial pressure during the sputtering of IGZO to optimize the property because the oxygen vacancy concentration governs the RS performance. Electrode selection is crucial and can impact the performance of the whole device. Thus, Cu TE was chosen for our second type of device because the diffusion of Cu ions can be beneficial for the formation of the conductive filament (CF). A ~5 nm SiO 2 barrier layer was employed between TE and RS layers to confine the diffusion of Cu into the RS layer. At the same time, this SiO 2 inserting layer can provide an additional interfacial series resistance in the device to lower the off current, consequently, improve the on/off ratio and whole performance. Finally, an oxygen affinity metal Ti was selected as the TE for our third type of device because the concentration of the oxygen atoms can be shifted towards the Ti electrode, which provides an oxygengettering activity near the Ti metal. This process may in turn lead to the formation of a sub-stoichiometric region in the neighboring oxide that is believed to be the origin of better performance. In conclusion, the transparent amorphous IGZO-based RRAMs were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of RRAMs, we integrated a set of TE materials, and a barrier layer on IGZO-based RRAM and compared the switch characteristics. Our encouraging results clearly demonstrate that IGZO is a promising material in RRAM applications and breaking the bottleneck of current memory technologies.more » « less
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/doi.org/10.1145/3411295.3411302
