IEEE MICROIEEE微处理器
IEEE MICRO(英文缩写 IEEE MICRO),ISSN 0272-1732,eISSN 1937-4143,中文译名:IEEE微处理器 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-27 至 2026-09-27，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 2.821 | Q2 |
| 2022 | 3.600 | Q2 |
| 2023 | 2.800 | Q2 |
| 2024 | 2.900 | Q2 |
| 2025 | 4.400 | Q1 |
IEEE MICRO 最新收录文献
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1. Yin-Yang: Programming Abstractions for Cross-Domain Multi-Acceleration.
PMID:期刊:FPGA accelerators offer performance and efficiency gains by narrowing the scope of acceleration to one algorithmic domain. However, real-life applications are often not limited to a single domain, which naturally makes -Domain -Acceleration a crucial next step. The challenge is, existing FPGA accelerators are built upon their specific vertically-specialized stacks, which prevents utilizing multiple accelerators from different domains. To that end, we propose a pair of dual abstractions, called Yin-Yang, which work in tandem and enable programmers to develop cross-domain applications using multiple accelerators on a FPGA. The Yin abstraction enables cross-domain algorithmic specification, while the Yang abstraction captures the accelerator capabilities. We also develop a dataflow virtual machine, dubbed XLVM, that transparently maps domain functions (Yin) to best-fit accelerator capabilities (Yang). With six real-world cross-domain applications, our evaluations show that Yin-Yang unlocks 29.4× speedup, while the best single-domain acceleration achieves 12.0×.
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2. ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks.
PMID:期刊:can significantly reduce the DNN computation and storage by decreasing the bitwidth of network encodings. However, without arduous manual effort, this deep quantization can lead to significant accuracy loss, leaving it in a position of questionable utility. We propose a systematic approach to tackle this problem, by automating the process of discovering the bitwidths through an end-to-end deep reinforcement learning framework (ReLeQ). This framework utilizes the sample efficiency of proximal policy optimization to explore the exponentially large space of possible assignment of the bitwidths to the layers. We show how ReLeQ can balance speed and quality, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks with minimal accuracy loss (≤ 0.3% loss) while minimizing the computation and storage costs. With these DNNs, ReLeQ enables conventional hardware and custom DNN accelerator to achieve 2.2× speedup over 8-bit execution.