Publication date: 24th July 2026
Analog Compute-in-Memory (ACiM) is an emerging computing paradigm that improves energy efficiency by performing computation directly within memory arrays, thereby reducing the costly movement of data between memory and processing units. Through highly parallel analog operations, ACiM offers the potential for low-power, high-throughput artificial intelligence computing.
Despite these advantages, practical ACiM systems face significant challenges arising from device variability, noise, limited precision, and nonideal weight-update characteristics. These imperfections can degrade inference accuracy and make efficient on-chip training particularly difficult. Requiring a single synaptic device to simultaneously satisfy the distinct demands of training and inference may also impose overly stringent device specifications.
To relax these requirements, we propose an ACiM memory hierarchy composed of two complementary synaptic-device technologies: one optimized for inference and the other for training. Efficiently integrating these devices requires close device–algorithm co-optimization. We explore three complementary strategies: (1) identifying existing algorithms that are inherently tolerant to device imperfections, (2) adapting conventional algorithms to better exploit the characteristics of analog hardware, and (3) developing new algorithmic frameworks that are intrinsically aligned with device physics.
These studies demonstrate that a heterogeneous memory hierarchy can provide a practical pathway toward efficient and scalable ACiM systems. More broadly, they highlight that tightly coupling device design with algorithm development—rather than optimizing either in isolation—is essential for fully realizing the potential of analog memory-based AI computing.
This work was supported by K-CHIPS (Korea Collaborative Hightech Initiative for Prospective Semiconductor Research) (2410010063, 20024709, 23040-15FC) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea), by the National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (RS-2025-07402970), and by Samsung Electronics.
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