Physically implemented analog in-memory distance computing with InGaZnO transistor-based capacitive unit

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract Modern edge devices increasingly require real-time adaptation to their environment without relying on cloud-based updates, which can introduce latency and security risks. To meet these demands, memory-augmented neural networks (MANNs) have gained traction for enabling adaptive on-device learning. Hardware implementations of MANNs commonly use non-volatile memory-based ternary content-addressable memory (TCAM), but their discrete outputs and write-verify steps limit compatibility with gradient-based learning. This work introduces analog in-memory distance computing (AIMDC), a unified architecture based on indium gallium zinc oxide (IGZO) thin-film transistors that performs both embedding extraction and similarity search using analog capacitive units (ACUs). By producing continuous, differentiable outputs directly from analog embeddings, AIMDC enables hardware-in-the-loop on-device representation learning without additional processing. We demonstrate energy-efficient one-shot learning accuracy comparable to a graphics processing unit but with up to a 576× improvement in energy efficiency. The high retention and endurance of the IGZO-based ACUs establish AIMDC as a scalable and robust solution for high-throughput, low-energy edge learning.

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