Mixed-Mode In-Memory Computing: Towards High-Performance Logic Processing In A Memristive Crossbar Array
Nan Du, Ilia Polian, Christopher Bengel, Kefeng Li, Ziang Chen, Xianyue Zhao, Uwe Huebner, Li-Wei Chen, Feng Liu, Massimiliano Di Ventra, Stephan Menzel, Heidemarie Krueger

TL;DR
This paper introduces a mixed-mode in-memory computing approach combining resistance and voltage operations within memory cells, enhancing reliability and efficiency for high-performance logic processing in memristive crossbar arrays.
Contribution
The work presents a novel mixed-mode computing method and a software tool for automated design, enabling reliable, high-speed, and dense in-memory logic operations.
Findings
Demonstrated digital adder with high accuracy
Achieved efficient encryption module component
Improved reliability without expensive current measurements
Abstract
In-memory computing is a promising alternative to traditional computer designs, as it helps overcome performance limits caused by the separation of memory and processing units. However, many current approaches struggle with unreliable device behavior, which affects data accuracy and efficiency. In this work, the authors present a new computing method that combines two types of operations,those based on electrical resistance and those based on voltage, within each memory cell. This design improves reliability and avoids the need for expensive current measurements. A new software tool also helps automate the design process, supporting highly parallel operations in dense two-dimensional memory arrays. The approach balances speed and space, making it practical for advanced computing tasks. Demonstrations include a digital adder and a key part of the encryption module, showing both strong…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Machine Learning and ELM
