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Analog Edge Inference with ReRAM

Researchers demonstrate “custom-designed system-on-chip (SoC) targeting AI applications with analog in-memory computing using resistive random-access memory (ReRAM) as the compute element.”

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Abstract

“As the demands of big data applications and deep learning continue to rise, the industry is increasingly looking to artificial intelligence (AI) accelerators. Analog in-memory computing (AiMC) with emerging nonvolatile devices enable good hardware solutions, due to its high energy efficiency in accelerating the multiply-and-accumulation (MAC) operation. Herein, an Applied Materials custom-designed system-on-chip (SoC) targeting AI applications with analog in-memory computing using resistive random-access memory (ReRAM) as the compute element is demonstrated. The first silicon achieves high energy efficiency in MAC operations. This chip is implemented with LeNet-1 neural network on ReRAM tiles and demonstrated by Modified National Institute of Standards and Technology (MNIST) classification with accuracy matching that predicted in the simulations. A simulation framework, AI Sim, is also developed to evaluate the system performance for large-scale application and guide the bitcell development and design choices.”

Find the open access technical article here, “A Fully Integrated System-on-Chip Design with Scalable Resistive Random-Access Memory Tile Design for Analog in-Memory Computing.” Published May 2022.

Cai, F., Yen, S., Uppala, A., Thomas, L., Liu, T., Fu, P., Zhang, X., Low, A., Kamalanathan, D., Hsu, J. and Ayyagari-Sangamalli, B. (2022), A Fully Integrated System-on-Chip Design with Scalable Resistive Random-Access Memory Tile Design for Analog in-Memory Computing. Adv. Intell. Syst. 2200014. https://doi.org/10.1002/aisy.202200014.



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