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Due to the separate memory and computation units in traditional Von-Neumann architecture, massive data transfer dominates the overall computing system’s power and latency, known as the ‘Memory-Wall’ issue. Especially with ever-increasing deep learning-based AI model size and computing complexity, it becomes the bottleneck for state-of-the-art AI computing systems. To address this challenge, In-Memory Computing (IMC) based Neural Network accelerators have been widely investigated to support AI computing within memory. However, most of those works focus only on inference. The on-device training and continual learning have not been well explored yet. In this work, for the first time, we introduce on-device continual learning with STT-assisted-SOT (SAS) Magnetic Random Access Memory (MRAM) based IMC system. On the hardware side, we have fabricated a SAS-MRAM device prototype with 4 Magnetic Tunnel Junctions (MTJ, each at 100nm × 50nm) sharing a common heavy metal layer, achieving significantly improved memory writing and area efficiency compared to traditional SOT-MRAM. Next, we designed fully digital IMC circuits with our SAS-MRAM to support both neural network inference and on-device learning. To enable efficient on-device continual learning for new task data, we present an 8-bit integer (INT8) based continual learning algorithm that utilizes our SAS-MRAM IMC-supported bit-serial digital in-memory convolution operations to train a small parallel reprogramming Network (Rep-Net) while freezing the major backbone model. Extensive studies have been presented based on our fabricated SAS-MRAM device prototype, cross-layer device-circuit benchmarking and simulation, as well as the on-device continual learning system evaluation.
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This content will become publicly available on December 1, 2026
Rapid learning with phase-change memory-based in-memory computing through learning-to-learn
Abstract There is a growing demand for low-power, autonomously learning artificial intelligence (AI) systems that can be applied at the edge and rapidly adapt to the specific situation at deployment site. However, current AI models struggle in such scenarios, often requiring extensive fine-tuning, computational resources, and data. In contrast, humans can effortlessly adjust to new tasks by transferring knowledge from related ones. The concept of learning-to-learn (L2L) mimics this process and enables AI models to rapidly adapt with only little computational effort and data. In-memory computing neuromorphic hardware (NMHW) is inspired by the brain’s operating principles and mimics its physical co-location of memory and compute. In this work, we pair L2L with in-memory computing NMHW based on phase-change memory devices to build efficient AI models that can rapidly adapt to new tasks. We demonstrate the versatility of our approach in two scenarios: a convolutional neural network performing image classification and a biologically-inspired spiking neural network generating motor commands for a real robotic arm. Both models rapidly learn with few parameter updates. Deployed on the NMHW, they perform on-par with their software equivalents. Moreover, meta-training of these models can be performed in software with high-precision, alleviating the need for accurate hardware models.
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- Award ID(s):
- 2318152
- PAR ID:
- 10570628
- Publisher / Repository:
- Nature Portfolio
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 16
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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