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Non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access memory (STT-MRAM) and spin-orbit torque magnetic random access memory (SOT-MRAM) have significant advantages compared to conventional SRAM due to their non-volatility, higher cell density, and scalability features. While previous work has investigated several architectural implications of NVM for generic applications, in this work we present DeepNVM, a framework to characterize, model, and analyze NVM-based caches in GPU architectures for deep learning (DL) applications by combining technologyspecific circuit-level models and the actual memory behavior of various DL workloads. We present both iso-capacity and isoarea performance and energy analysis for systems whose lastlevel caches rely on conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies. In the iso-capacity case, STT-MRAM and SOT-MRAM provide up to 4.2× and 5× energy-delay product (EDP) reduction and 2.4× and 3× area reduction compared to conventional SRAM, respectively. Under iso-area assumptions, STT-MRAM and SOT-MRAM provide 2.3× EDP reduction on average across all workloads when compared to SRAM. Our comprehensive cross-layer framework is demonstrated on STT-/SOT-MRAM technologies and can be used for the characterization, modeling, and analysis of any NVM technology for last-level caches in GPU platforms for deep learning applications.
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This content will become publicly available on June 11, 2026
Energy-Efficient Stochastic Signal Manipulation in Superparamagnetic Tunnel Junctions via Voltage-Controlled Exchange Coupling
Superparamagnetic tunnel junctions (sMTJs) are emerging as promising components for stochastic units in neuromorphic computing owing to their tunable random switching behavior. Conventional MTJ control methods, such as spin-transfer torque (STT) and spin–orbit torque (SOT), often require substantial power. Here, we introduce the voltage-controlled exchange coupling (VCEC) mechanism, enabling the switching between antiparallel and parallel states in sMTJs with an ultralow power consumption of only 40 nW, approximately 2 orders of magnitude lower than conventional STT-based sMTJs. This mechanism yields a sigmoid-shaped output response, making it ideally suited to neuromorphic computing applications. Furthermore, we validate the feasibility of integrating VCEC with SOT current control, offering an additional dimension for magnetic state manipulation. This work marks the first practical demonstration of the VCEC effect in sMTJs, highlighting its potential as a low-power control solution for probabilistic bits in advanced computing systems.
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- Award ID(s):
- 2230963
- PAR ID:
- 10642807
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Journal Name:
- Nano Letters
- Volume:
- 25
- Issue:
- 23
- ISSN:
- 1530-6984
- Page Range / eLocation ID:
- 9181 to 9188
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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