Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems

• URL: http://arxiv.org/abs/2507.01429v1
• Date: Wed, 02 Jul 2025 07:29:53 GMT
• Title: Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems
• Authors: Benjamin Chen Ming Choong, Tao Luo, Cheng Liu, Bingsheng He, Wei Zhang, Joey Tianyi Zhou,
• Abstract summary: racetrack memory is a non-volatile technology that allows high data density fabrication.<n>In-memory arithmetic circuits with memory cells affects both the memory density and power efficiency.<n>We present an efficient in-memory convolutional neural network (CNN) accelerator optimized for use with racetrack memory.
• Score: 54.045712360156024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks generate and process large volumes of data, posing challenges for low-resource embedded systems. In-memory computing has been demonstrated as an efficient computing infrastructure and shows promise for embedded AI applications. Among newly-researched memory technologies, racetrack memory is a non-volatile technology that allows high data density fabrication, making it a good fit for in-memory computing. However, integrating in-memory arithmetic circuits with memory cells affects both the memory density and power efficiency. It remains challenging to build efficient in-memory arithmetic circuits on racetrack memory within area and energy constraints. To this end, we present an efficient in-memory convolutional neural network (CNN) accelerator optimized for use with racetrack memory. We design a series of fundamental arithmetic circuits as in-memory computing cells suited for multiply-and-accumulate operations. Moreover, we explore the design space of racetrack memory based systems and CNN model architectures, employing co-design to improve the efficiency and performance of performing CNN inference in racetrack memory while maintaining model accuracy. Our designed circuits and model-system co-optimization strategies achieve a small memory bank area with significant improvements in energy and performance for racetrack memory based embedded systems.

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