Publication date: 15th April 2025
Artificial intelligence is pushing the limits of digital computing to such an extent that, if current trends continue, global energy consumption from computation alone would eclipse all other forms of energy in the next two decades [1]. Consequently, it is crucial to explore new strategies that can reduce energy consumption and increase computational speed to meet growing demands. One promising approach is in-memory analog computing, which has the potential to deliver significant improvements in both efficiency and speed. However, designing analog computing systems presents considerable challenges. First, analog computing necessitates a fundamental rethinking of computation at the material level, where information is now stored as continuously variable physical observables. This shift introduces difficulties related to the accuracy, dynamic range, and reliability of analog systems—issues that prompted the transition to digital computing nearly a century ago. Second, digital computation has traditionally relied on a reductionist hierarchy, allowing for the separate design of devices, architectures, and algorithms without sensitivity to one another. In contrast, analog computing systems are inherently interdependent from the material to architectural level, and this sensitivity can be advantageous. I will discuss two approaches to in-memory compute: first, on developing accelerators for neural networks using analog memory, and second, on developing dynamic devices which more closely mimic the brain. In the case of memory devices, I will focus on newly developed electro-thermo-chemical random-access memory (ETCRAM) [2]. ETCRAM exhibits a dynamic range of more than six decades of analog tunable resistance with thousands of available states, deterministic write operations with high accuracy, low programming voltages, current-voltage linearity across all decades, and programming speeds as fast as 15 ns. These features, when combined in a single device, heralds a massively improved set of benchmarks for analog compute. Second, I will discuss our work on designing artificial axons, neurons, and networks using spin-crossover oxides[3,4].
[1] Based on SRC Decadal Report (2021), adapted from EES2 report
[2] Talin, Alec, et al. "Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities." Chemical Reviews (2025).
[3] Brown, Timothy D., et al. "Axon-like active signal transmission." Nature 633.8031 (2024): 804-810.
[4] Zhang, Alan, et al. "Tuning the Spin Transition and Carrier Type in Rare‐Earth Cobaltates via Compositional Complexity." Advanced Materials 36.47 (2024): 2406885.
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