Year: 2025
Type: article
Abstract: This paper presents a novel transposed MRAM architecture (WinEdge) specifically optimized for Winograd convolution acceleration in edge computing devices. Leveraging Magnetic Tunnel Junctions (MTJs) with Spin Hall Effect (SHE)-assisted Spin-Transfer Torque (STT) writing, the proposed design enables a single SHE current to simultaneously write data to four MTJs, substantially reducing power consumption. Additionally, the integration of stacked MTJs significantly improves storage density. The proposed WinEdge efficiently supports both standard and transposed data access modes regardless of bit-width, achieving up to 36% lower power, 47% reduced energy consumption, and 28% faster processing speed compared to existing designs. Simulations conducted in 45 nm CMOS technology validate its superiority over conventional SRAM-based solutions for convolutional neural network (CNN) acceleration in resource-constrained edge environments.
Source: Electronics
Institution: University of Illinois Chicago
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: National Science Foundation
Awards: CAREER: Elastic Intermittent Computation Enabling Batteryless Edge Intelligence, CSR: Small: Cross-Layer Solutions Enabling Instant Computing for Edge Intelligence Devices, Collaborative Research: SaTC: CORE: Medium: Security and Robustness for Intermittent Computing Using Cross-Layer Post-CMOS Approaches
OpenAlex ID: W4411457484