A smarter way to power artificial intelligence
A fully integrated compute-in-memory system pairs 2D memristors with silicon selectors to offer a practical, energyefficient route for powering next-gen AI applications.
ith a simple click, your hastily taken photo sharpens, a garbled voice message turns into polished text and a chatbot drafts an email in perfect prose. Today’s digital tools, enhanced by artificial intelligence (AI), seem to perform magic on demand.
Associate Professor Ang Kah Wee and his team developed a fully integrated CIM system that stores and processes data in the same physical space.
But behind every interaction lies an unseen cost. Running state-of-the-art AI models is tied to staggering amounts of computing power — and gobbles up massive amounts of energy. Training a large language model like GPT-3, for instance, comes with a price tag upwards of $10 million. You’ll also need more than 700,000 litres of water to do that. Each query or task that follows continues to tap into energy-hungry infrastructure to retrieve, compute and respond.
Part of the problem is the machines themselves. Most computers today are still built around hardware introduced over 75 years ago, and rely on the transistor device first invented in 1947. This arrangement has served generalpurpose computing well for decades, but it creates a data traffic jam for AI, as it separates compute and memory. This means data must constantly shuttle between memory and processor — slamming figurative brakes on workflows while guzzling disproportionate amounts of power.
“AI workloads are memory-centric,” says Associate Professor Ang Kah Wee from the Department of Electrical and Computer Engineering at the College of Design and Engineering, National University of Singapore. “It’s not the computing that takes time or power — it’s all the moving of data.”
Indeed, this bottleneck is becoming increasingly unsustainable as AI enters the vocabulary of everyday people. And amidst this challenge has emerged the concept of “compute-in-memory” (CIM), which harnesses memristors to process and store data all in one place.
Unlike traditional transistors, which rely on the movement of electrons and lose data when powered down, memristors work more like the human brain. They use ions to carry information and can “remember” their resistance state even without power. This allows them to store and process data at the same location — eliminating the need for constant data transfers between separate memory and computing units.
However, making them work at scale, especially with advanced two-dimensional (2D) materials that are all the buzz, has remained an engineering challenge, stymied by control issues, signal interference and limited integration with conventional circuitry.
Issue 05 | May 2025
A new kind of memory machine
Forging New Frontiers
Assoc Prof Ang led a team to develop a fully integrated CIM system that stores and processes data in the same physical space. Detailed in their Nature Communications paper published on 19 March 2025, the system is built around a 32x32 array of memristors made from hafnium diselenide, an ultra-thin 2D material with low energy requirements and rapid switching speeds. A key element of the design is the silicon-based selector that sits beneath each memristor.
“These selectors act like traffic controllers to ensure that only the targeted memristor switches on, while others remain unaffected.”
“These selectors act like traffic controllers to ensure that only the targeted memristor switches on, while others remain unaffected,” explains Assoc Prof Ang. “This averts unwanted electrical interference, known as sneak current, that can corrupt data in large memristor arrays.”
Pairing each memristor with a selector in a one-selector-one-memristor configuration forms a tightly packed, high controllable network. Further, the method used to assemble the device allows the fragile memristor switching layer to be transferred directly onto a silicon substrate without damage — making it compatible with existing semiconductor manufacturing techniques.
The team went on to build a complete working system by integrating peripheral circuitry to manage inputs, outputs and computations. Instead of relying on conventional analog-to-digital converters, which are bulky and power-hungry, they designed time-domain sensing circuits that interpret electrical signals by measuring how long it takes for voltage changes to occur. This approach speeds up data readout and uses less than half the power of traditional methods.
The system also takes advantage of the natural non-linear behaviours of these circuits to implement built-in activation functions — essential components in neural networks that mimic how biological neurons “fire”. When embedded directly into hardware, the activation functions help the system avoid additional processing steps to further improve efficiency.
05 | May 2025
The outcome is a fast, compact and energy-efficient CIM platform. The memristors switch in nanoseconds and can endure more than 26,000 programming cycles without degradation. When tested on a pattern recognition task using a simple convolutional neural network, the system achieved 97.5% accuracy — a level comparable to conventional digital systems, but at a fraction of the energy cost.
From lab to fab
“Being siliconcompatible, our method also doesn’t require clean-sheet manufacturing nor exotic materials.”
Merging memory and processing into one architecture reduces latency and energy demand while increasing throughput — all within a compact device. This approach could be a game-changer for applications where power is limited but performance is critical, such as AI-based edge computing and autonomous systems.
“Being silicon-compatible, our method also doesn’t require clean-sheet manufacturing nor exotic materials,” adds Assoc Prof Ang. “We think it’s very practical for realworld AI hardware.”
The team is exploring ways to expand the array size and handle more complex datasets, with an eye toward real-time deployment. Interestingly, the combination of fast switching, reliable endurance and low-voltage operation makes their system particularly well-suited for neuromorphic computing — hardware that mimics how the brain processes information.
