WeChat Mini Program
Old Version Features

Speeding-Up Successive Read Operations of STT-MRAM Via Read Path Alternation for Delay Symmetry

Design, Automation, and Test in Europe(2025)

School of Electrical Engineering

Cited 0|Views0
Abstract
Recent research on data-intensive computing systems has demonstrated that system throughput and latency are critically dependent on memory read bandwidth, highlighting the need for fast memory read operations. Although spin-transfer torque magnetic random-access memory (STT-MRAM) has emerged as a promising alternative to CMOS-based embedded memories, STT -MRAM continues to face challenges related to read speed and energy efficiency. This paper introduces a novel read scheme that enhances read speed and energy in successive read operations by alternating read paths between data and reference cells. This approach effectively mitigates worst-case read scenarios by balancing the read voltage swings. HSPICE simulations using 28nm CMOS technology show a 31.5% improvement in read speed and 48.8% reduction in energy consumption compared to the previous approach. SCALE-Sim system simulations also demonstrate that applying the proposed read scheme to STT-MRAM embedded memories in AI accelerators shows a significant reduction in memory energy for CNN inference tasks compared to the SRAM embedded memory.
More
Translated text
Key words
STT-MRAM,Energy Efficient,High-Speed Read,Delay Imbalance
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined