All-in-Memory Stochastic Computing using ReRAM
De Lima JPC, Moghadam MS, Aygun S, Castrillon J, Najafi MH, Khan AA (2025)
Publication Type: Conference contribution
Publication year: 2025
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: Proceedings - Design Automation Conference
Event location: San Francisco, CA, USA
ISBN: 9798331503048
DOI: 10.1109/DAC63849.2025.11132096
Abstract
As the demand for efficient, low-power computing in embedded and edge devices grows, traditional computing methods are becoming less effective for handling complex tasks. Stochastic computing (SC) offers a promising alternative by approximating complex arithmetic operations, such as addition and multiplication, using simple bitwise operations, like majority or AND, on random bit-streams. While SC operations are inherently fault-tolerant, their accuracy largely depends on the length and quality of the stochastic bit-streams (SBS). These bit-streams are typically generated by CMOS-based stochastic bit-stream generators that consume over 80% of the SC system's power and area. Current SC solutions focus on optimizing the logic gates but often neglect the high cost of moving the bit-streams between memory and processor. This work leverages the physics of emerging ReRAM devices to implement the entire SC flow in place: generating low-cost true random numbers and SBSs, conducting SC operations, and converting SBSs back to binary. Considering the low reliability of ReRAM cells, we demonstrate how SC's robustness to errors copes with ReRAM's variability. Our evaluation shows significant improvements in throughput (1.39 ×, 2.16 ×) and energy consumption (1.15 ×, 2.8 ×) over state-of-the-art (CMOS- and ReRAM-based) solutions, respectively, with an average image quality drop of 5% across multiple SBS lengths and image processing tasks.
Involved external institutions
Technische Universität Dresden
Germany (DE)
Case Western Reserve University
United States (USA) (US)
University of Louisiana at Lafayette (UL Lafayette)
United States (USA) (US)
Germany (DE)
Case Western Reserve University
United States (USA) (US)
University of Louisiana at Lafayette (UL Lafayette)
United States (USA) (US)
How to cite
APA:
De Lima, J.P.C., Moghadam, M.S., Aygun, S., Castrillon, J., Najafi, M.H., & Khan, A.A. (2025). All-in-Memory Stochastic Computing using ReRAM. In Proceedings - Design Automation Conference. San Francisco, CA, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
De Lima, Joao Paulo C., et al. "All-in-Memory Stochastic Computing using ReRAM." Proceedings of the 62nd ACM/IEEE Design Automation Conference, DAC 2025, San Francisco, CA, USA Institute of Electrical and Electronics Engineers Inc., 2025.
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