# EAGER: Accelerating Scalable Stochastic Neuro-Inspired Computing With Spintronics: Devices to Systems

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Iowa State University (IA) · $238,794

## Abstract

Remarkable advances in Artificial Intelligence (AI) have demonstrated near-human cognitive performance in various applications. However, state-of-the-art AI still exhibits a large (orders of magnitude) efficiency gap compared to human brains. Enabling efficient AI hardware/software systems will be the key to deploying AI in various domains, including transportation, healthcare, and defense. Taking cues from the biological brains, neuro-inspired computing recently emerges as a promising approach to addressing the computational efficiency challenges. However, neuro-inspired computing with the complementary metal-oxide-semiconductor (CMOS) digital hardware lacks flexibility and efficiency due to mismatch at various levels from device to architecture. This project will leverage novel magneto-electronic (spintronic) technologies to create efficient and robust computational components that emulate neural stochastic functionality. The components will be integrated into in-memory computing architectures and co-designed with bio-inspired learning algorithms to achieve advanced cognitive capabilities. This project will significantly advance the science of developing next-generation AI hardware with emerging technologies. By implementing device-to-system co-design for stochastic in-memory computing, this project will create interdisciplinary knowledge of device integration, computing architecture design, and algorithm development. Such knowledge is crucial for addressing the challenges 

## Key facts

- **NSF award ID:** 2534279
- **Awardee organization:** Iowa State University (IA)
- **SAM.gov UEI:** DQDBM7FGJPC5
- **PI:** Cheng Wang
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Neuromorphic Computing, EAGER, EXP PROG TO STIM COMP RES
- **Estimated total:** $238,794
- **Funds obligated:** $238,794
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2534279

## Citation

> US National Science Foundation, Award 2534279, EAGER: Accelerating Scalable Stochastic Neuro-Inspired Computing With Spintronics: Devices to Systems. Retrieved via AI Analytics 2026-06-09 from https://api.ai-analytics.org/grant/nsf/2534279. Licensed CC0.

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