Device Noises Resilient Training and Inference Framework for Smart Sensing on Analog Computing In Memory
Journal
Proceedings - 2025 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, CASES 2025
Start Page
21
End Page
22
ISBN
[9798400719912]
Date Issued
2025-09-28
Author(s)
Abstract
Neural networks have demonstrated superior performance over rule-based and model-based approaches in processing noisy sensing data. However, their substantial computational and energy demands hinder deployment in battery-powered embedded systems. Computing-in-Memory (CIM) devices offer a promising alternative by significantly reducing energy consumption. Prior work [2] achieves this by leveraging a non-von Neumann architecture, which minimizes data movement between memory and compute units, thereby mitigating the memory wall bottleneck. Despite these advantages, analog CIM (ACIM) systems face several key challenges, including analog noise, limited numerical precision, and increased hardware complexity. While cloud-based neural networks are still dominant, emerging applications increasingly demand real-time, privacy-preserving inference on-device. For instance, facial authentication requires local execution on edge devices to ensure low-latency responsiveness and to protect user privacy. CIM architectures are particularly well-suited to these scenarios due to their tightly integrated memory-compute structure, offering low-latency and energy-efficient inference capabilities.
Event(s)
2025 IEEE/ACM International Conference on Compilers, Architectures and Synthesis for Embedded Systems, CASES 2025
Publisher
ACM
Type
conference paper