MANIC: A Vector-Dataflow Architecture for Ultra-Low-Power Embedded Systems

MANIC: A Vector-Dataflow Architecture for Ultra-Low-Power Embedded Systems
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DOI:
10.1145/3352460.3358277
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发表时间:
2019-10
期刊:
Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture
影响因子:
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通讯作者:
Graham Gobieski;Amolak Nagi;Nathan Serafin;Mehmet Meric Isgenc;Nathan Beckmann;Brandon Lucia
Graham Gobieski;Amolak Nagi;Nathan Serafin;Mehmet Meric Isgenc;Nathan Beckmann;Brandon Lucia
中科院分区:
其他
文献类型:
--
作者:
Graham Gobieski;Amolak Nagi;Nathan Serafin;Mehmet Meric Isgenc;Nathan Beckmann;Brandon Lucia

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超低功耗传感器节点使许多新的应用成为可能,并且变得越来越普遍和重要。能源效率是这些设备价值的关键决定因素:电池供电的节点希望其电池持续使用,而收集能源的节点应该尽量减少充电时间。不幸的是,目前的设备是能量效率低的。在这项工作中,我们提出了MANIC,一种新的,高能效的架构,针对超低功耗传感器域。MANIC在保持可编程性和通用性的同时实现了高能效。MANIC引入了向量并行执行,允许它利用向量指令序列中的并行执行,并将指令提取和解码分摊到整个操作向量上。通过将值从生产者转发到消费者,MANIC避免了昂贵的向量寄存器文件访问。通过仔细地调度代码和避免死寄存器写入,MANIC避免了昂贵的向量寄存器写入。在七个基准测试中,MANIC的能效平均比纯量基线高2.8倍,比矢量基线高38.1%,并且与理想设计的能效相差不到26.4%。
Ultra-low-power sensor nodes enable many new applications and are becoming increasingly pervasive and important. Energy efficiency is the key determinant of the value of these devices: battery-powered nodes want their battery to last, and nodes that harvest energy should minimize their time spent recharging. Unfortunately, current devices are energy-inefficient. In this work, we present MANIC, a new, highly energy-efficient architecture targeting the ultra-low-power sensor domain. MANIC achieves high energy-efficiency while maintaining programmability and generality. MANIC introduces vector-dataflow execution, allowing it to exploit the dataflows in a sequence of vector instructions and amortize instruction fetch and decode over a whole vector of operations. By forwarding values from producers to consumers, MANIC avoids costly vector register file accesses. By carefully scheduling code and avoiding dead register writes, MANIC avoids costly vector register writes. Across seven benchmarks, MANIC is on average 2.8× more energy efficient than a scalar baseline, 38.1% more energy-efficient than a vector baseline, and gets to within 26.4% of an idealized design.