A Nature Electronics Cover Article Published by ECE’s Qiangfei Xia and Colleagues Reports on Pioneering Signal-processing System
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The evolution of wireless-communication technology and the Internet of Things demands radiofrequency communication systems with higher energy efficiency and faster communication speeds. To meet this challenge, research by Qiangfei Xia – the Dev and Linda Gupta Professor in the UMass Amherst Electrical and Computer Engineering (ECE) Department – and his collaborators has developed a trailblazing radiofrequency signal-processing system, based on analogue in-memory computing within a memristive system-on-a-chip, that is much faster and more energy efficient than today’s state-of-the-art digital-processing systems. Xia and seven of his colleagues have reported on this breakthrough in the prestigious journal Nature Electronics. This work is also featured on the cover of the July issue, marking the third cover highlight for the Xia group at Nature Electronics, including on the inaugural cover of this journal.
As Xia and his team explain in their Nature Electronics paper, “The memristive system-on-a-chip offers an identification accuracy of over 90 percent and is up to 6.8 times more energy efficient and up to 6.2 times faster than traditional digital-processing platforms.”
In addition to Xia, the researchers who collaborated on the Nature Electronics paper are: Yi Huang (UMass ECE), Chaoyi He (Texas A&M University), Yunzhi Ling (UMass ECE), Ning Ge (TetraMem Inc., San Jose, California), J. Joshua Yang (USC & TetraMem), Miao Hu (TetraMem), and Linda Katehi (Texas A&M).
According to the research team, as artificial intelligence (AI) becomes increasingly integrated into our daily lives, the hardware required to run these powerful algorithms is consuming an ever-increasing amount of energy. This growing demand makes it challenging to bring AI to energy-limited environments, such as wireless communications at the edge of networks.
“To address this challenge,” as Xia and his team explain, “researchers are turning to a novel computing paradigm known as analogue in-memory computing (AIMC), which is implemented with emerging components like memristors. Unlike traditional computing architectures that rely on frequent data movement between separate memory and processing units, AIMC performs computations directly within memory by exploiting the fundamental laws of physics, resulting in faster and more energy-efficient processing.”
As the website Nanowerk explains, “A memristor is a non-volatile device that combines memory and resistor functions and can be used for neuromorphic computing and quantum computing.”
According to the Xia team, “We have dedicated over a decade to advancing memristor-based AIMC hardware, from developing high-performance memristors to building system-on-a-chip solutions. This sustained effort has culminated in our latest breakthrough: a memristive system-on-a-chip that enables direct processing of analogue radiofrequency signals within memory. This system brings AI closer to the network edge, where low-power consumption and high-speed processing are critical.”
The academic research and proof-of-concept demonstrations developed by the Xia’s Nanodevices and Integrated Systems Lab have been instrumental in the launch of TetraMem Inc., a Silicon-Valley-based startup specializing in the design of next-generation AI accelerators. Several co-authors that collaborated on the Nature Electronics paper are affiliated with TetraMem, which developed the MX100 evaluation kit used in this study. These demonstrations also benefited from close collaboration with radiofrequency experts from Texas A&M.
As Xia and his team summarize their revolutionary research, “Such efforts will be critical for advancing signal-processing capabilities in complex, real-world, wireless-communication environments.” (July 2025)