New memristor-based system could boost processing of radiofrequency signals

The development of more advanced technologies to process radiofrequency signals could further advance wireless communication, allowing devices connected to the internet to share information with each other faster and while consuming less energy. Currently, radio frequency signals are processed using software-defined radios (SDRs), systems that can modulate, filter and analyze signals using software rather than hardware components.

Despite their widespread use, these systems rely on purely digital hardware in which computing and memory modules are physically separated, leading to constant data shuttling between the two and hence extra energy consumption. Furthermore, the extensive use of circuit components known as analog-to-digital converters (ADCs), which convert incoming radiofrequency signals into digital values that can then be processed by digital computers, often results in processing delays (i.e., latency) and substantial energy consumption. Electronics engineers have thus been trying to develop alternative systems that can directly manipulate signals in their original (i.e., analog) form, which would reduce the movement of data and lower energy consumption.

Researchers at the University of Massachusetts Amherst, Texas A&M University and TetraMem Inc. recently introduced a promising new system for processing analog radiofrequency systems, which is based on non-volatile memory devices known as memristors integrated on a chip. Their proposed system, presented in a paper in Nature Electronics, was found to process radiofrequency signals significantly faster and more energy-efficiently than existing SDRs.

“This work was inspired by the brain’s approach to processing sensory signals for information extraction,” the authors told Tech Xplore. “Unlike state-of-the-art microelectronic sensors, which convert analog signals into digital bits which are then stored and retrieved for processing later, the brain processes analog signals directly, in real time and within memory, retaining only information that remains persistent. This enables the brain to quickly extract critical information with minimal energy consumption.”

As part of their recent study, the authors developed a memristive system-on-a-chip (SoC), an integrated circuit that contains all the essential components of a computing system along with memristors. This SoC was designed to emulate the human brain’s ability to process and rapidly extract important information.

The integrated circuit contains a crossbar array of memristors, a circuit architecture that connects inputs and outputs following a grid-like structure. This array of memristors can process sensory signals sampled in real-time, and its configuration effectively reflects the weights of a customized AI algorithm.

“The memristive SoC is the result of over a decade of dedicated research into emerging artificial intelligence (AI) hardware,” the authors say.

“Our journey spans from developing high-performance memristors to designing integrated chips compatible with commercial foundries, and ultimately, to commercializing memristive hardware for next-generation AI and communications. The SoC demonstrated processing speeds and energy efficiencies orders of magnitude superior to conventional sensor-processing chips.”

The new integrated circuit developed by the researchers directly processes analog signals stored by memristors, extracting embedded information and reliably classifying important signals, mimicking how the brain extracts valuable information. The team evaluated the memristive SoC in a series of tests and found that it could process radiofrequency signals with low latency, while also minimizing power consumption.

“This approach enables ultra-low-latency and energy-efficient signal processing directly on edge devices,” the authors explained. “The RF signal processing architecture distributes both signal processing and neural network inference tasks across ten computing cores, supported by fully integrated on-chip peripheral circuitry.

“We demonstrate the system’s capabilities through high-accuracy RF transmitter identification and anomaly detection, achieving significantly lower latency and higher energy efficiency compared to state-of-the-art digital signal processing platforms.”

The team’s newly developed memristive SoC integrates AI directly into wireless communication, leveraging the unique qualities of memristors. In the future, its underlying design could inspire the development of other similar memristor-based integrated circuits, potentially contributing to the real-world deployment of faster and more energy-efficient wireless communications systems.

“The system introduced in our recent paper is a proof of concept, yet it is a fundamental first step,” the authors added. “We are currently developing our next-generation scaled-up memristive system and advanced RF circuits to support higher frequencies and expanded functionalities.

“Our goal is to integrate our emerging hardware with existing Wi-Fi standards and future sixth-generation (6G) networks, enabling embedded AI to perform more efficient, adaptive RF signal processing in increasingly dynamic and complex wireless environments.”

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