Given the high economic and environmental costs of using large vision or language models, analog in-memory accelerators present a promising solution for energy-efficient AI. While inference on analog accelerators has been studied recently, the training perspective is underexplored. Recent studies have shown that the workhorse of digital AI training - stochastic gradient descent (SGD) algorithm converges inexactly when applied to model training on non-ideal devices. This paper puts forth a theoretical foundation for gradient-based training on analog devices. We begin by characterizing the non-convergent issue of SGD, which is caused by the asymmetric updates on the analog devices. We then provide a lower bound of the asymptotic error to show that there is a fundamental performance limit of SGD-based analog training rather than an artifact of our analysis. To address this issue, we study a heuristic analog algorithm called Tiki-Taka that has recently exhibited superior empirical performance compared to SGD. We rigorously show its ability to converge to a critical point exactly and hence eliminate the asymptotic error. The simulations verify the correctness of the analyses.

While inference on analog accelerators has been studied recently, the training perspective is underexplored. Recent studies have shown that the "workhorse" of digital AI training - stochastic gradient descent (

While inference on analog accelerators has been studied recently, the training perspective is underexplored. Recent studies have shown that the "workhorse" of digital AI training - stochastic gradient descent (

Given the high economic and environmental costs of using large vision or language models, analog in-memory accelerators present a promising solution for energy-efficient AI. While inference on analog accelerators has been studied recently, the training perspective is underexplored. Recent studies have shown that the "workhorse" of digital AI training - stochastic gradient descent (SGD) algorithm converges inexactly when applied to model training on non-ideal devices. This paper puts forth a theoretical foundation for gradient-based training on analog devices. We begin by characterizing the non-convergent issue of SGD, which is caused by the asymmetric updates on the analog devices.

An Iranian-American citizen and a Swiss Iranian have been arrested and charged by United States authorities with allegedly exporting sensitive technology to Iran that was used in a deadly drone attack on American forces based in Jordan. Islamic Resistance in Iraq, an umbrella group of Iran-backed fighters, was alleged to have carried out the drone attack that killed three US soldiers and wounded 47 others at a US military outpost in Jordan, near the Syrian border, in January. Federal prosecutors in Boston on Monday charged 38-year-old Mohammad Abedininajafabadi, who is known as Mohammad Abedini, the co-founder of an Iranian-based company, and Mahdi Sadeghi, 42, an employee of Massachusetts-based semiconductor manufacturer Analog Devices, with conspiring to violate US export laws. Abedini, a dual citizen of Switzerland and Iran, was arrested in Milan, Italy, at the request of the US government, which will seek his extradition. Sadeghi, an Iranian-born naturalised US citizen, who lives in Natick, Massachusetts, was also arrested.

Given the high economic and environmental costs of using large vision or language models, analog in-memory accelerators present a promising solution for energy-efficient AI. While inference on analog accelerators has been studied recently, the training perspective is underexplored. Recent studies have shown that the "workhorse" of digital AI training - stochastic gradient descent (SGD) algorithm converges inexactly when applied to model training on non-ideal devices. This paper puts forth a theoretical foundation for gradient-based training on analog devices. We begin by characterizing the non-convergent issue of SGD, which is caused by the asymmetric updates on the analog devices. We then provide a lower bound of the asymptotic error to show that there is a fundamental performance limit of SGD-based analog training rather than an artifact of our analysis. To address this issue, we study a heuristic analog algorithm called Tiki-Taka that has recently exhibited superior empirical performance compared to SGD and rigorously show its ability to exactly converge to a critical point and hence eliminates the asymptotic error. The simulations verify the correctness of the analyses.

Are you a problem solver looking for a hands-on internship position with a market-leading company that will help develop your career and reward you intellectually and professionally? Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $12 billion in FY22 and approximately 25,000 people globally working alongside 125,000 global customers, ADI ensures today's innovators stay Ahead of What's Possible. At ADI, you will learn from the brightest minds who are here to help you grow and succeed.

Oakland, California – When Silicon Valley chipmaker Marvell learned that one of its chips was found in a Russian surveillance drone recovered in 2016, it set out to investigate how that came to be. The chip, which costs less than $2, was shipped in 2009 to a distributor in Asia, which sold it to another broker in Asia, which later went out of business. "We couldn't trace it any further," Marvell Technology Group Chief Operations Officer Chris Koopmans said in a recent interview. Years later, it reappeared in the drone recovered in Lithuania. Marvell's experience is one of myriad examples of how chipmakers lack ability to track where many of their lower-end products end up, executives and experts said.

AI/Edge Vastai Technologies is using Arteris IP's FlexNoC Interconnect IP and AI Package for its Artificial Intelligence Chips for artificial intelligence and computer vision systems-on-chip (SoCs). Startup Vastai Technologies was founded in December 2018, designs ASICs and software platforms for computer vision and AI applications, such as smart city, smart surveillance, smart education, according to a press release. Smart city connections will be dominated by video surveillance and smart utility metering, says ABI Research in a report, predicting that by 2026, 87% of the smart city market will be those two device types. Low-latency 5G connections and embedded AI in video surveillance systems are some of the enabling technologies. Internet of Things The smart building market will generate over $2 billion in revenue by 2026 for software and services, says ABI Research, thanks to some new emerging applications.

AI workloads are different from the calculations most of our current computers are built to perform. AI implies prediction, inference, intuition. But the most creative machine learning algorithms are hamstrung by machines that can't harness their power. Hence, if we're to make great strides in AI, our hardware must change, too. Let's start in the present, with applying massively distributed deep learning algorithms to Graphics processing units (GPU) for high speed data movement, to ultimately understand images and sound.