Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation

Cardwell, Suma; Schuman, Catherine D.; Smith, J. Darby; Patel, Karan; Kwon, Jaesuk; Liu, Samuel; Allemang, Christopher; Misra, Shashank; Incorvia, Jean Anne C.; Aimone, James B.

doi:10.1109/icrc57508.2022.00008

Cited by 2 publications

(1 citation statement)

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“…AI-guided codesign can further alleviate the challenges of interdisciplinary codesign and accelerate design. 372,373 AI-enhanced codesign techniques can use the constraints from emerging devices to develop algorithmic solutions for a given application. This can not only accelerate design but also enable interactions between experts from different areas of the microelectronics design stack including theory, algorithms, circuits, devices, and materials.…”

Section: Analog Circuit Designmentioning

confidence: 99%

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

et al. 2023

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Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

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Section: Analog Circuit Designmentioning

confidence: 99%

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

et al. 2023

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AI in Performance Management

Thirunagalingam,

Addanki,

Vemula

et al. 2024

Advances in Business Strategy and Competitive Advantage

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Artificial Intelligence (AI) is revolutionizing many facets of modern business, and one of the most transformative areas is performance management. Traditionally, performance management has been characterized by annual reviews, subjective evaluations, and a focus on past performance. However, AI is redefining this landscape, introducing new methodologies and tools that offer more dynamic, objective, and data-driven approaches to managing and enhancing employee performance.Moreover, AI enhances the feedback process by providing actionable insights that are often more specific and relevant than those derived from traditional review methods. For instance, AI can highlight recurring issues that might not be immediately obvious to human reviewers, enabling proactive management and support. By analyzing individual performance data, AI can recommend tailored training and development opportunities that align with each employee's strengths, weaknesses, and career goals.

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Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation

Cited by 2 publications

References 20 publications

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

AI in Performance Management

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