Prefetching Using Principles of Hippocampal-Neocortical Interaction
Prefetching Using Principles of Hippocampal-Neocortical Interaction
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DOI：
10.1145/3593856.3595901
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Michael Wu;Ketaki Joshi;Andrew Sheinberg;Guilherme Cox;Anurag Khandelwal;Raghavendra Pradyumna Pothukuchi;A. Bhattacharjee
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文献类型：
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作者：
Michael Wu;Ketaki Joshi;Andrew Sheinberg;Guilherme Cox;Anurag Khandelwal;Raghavendra Pradyumna Pothukuchi;A. Bhattacharjee
内存预取提高了许多系统层的性能。然而，随着存储器层次结构和应用程序存储器访问模式变得更加复杂，以低开销实现高预取精度是具有挑战性的。此外，预取器在新的访问模式出现时适应它们的能力变得比以往任何时候都更加重要。最近的工作证明了使用深度学习技术来提高预取精度，尽管存在不切实际的计算和存储开销。这篇论文建议从人脑的学习机制和记忆结构中获得灵感-特别是海马体和新皮质-建立资源高效、准确和适应性强的预取器。
Memory prefetching improves performance across many systems layers. However, achieving high prefetch accuracy with low overhead is challenging, as memory hierarchies and application memory access patterns become more complicated. Furthermore, a prefetcher's ability to adapt to new access patterns as they emerge is becoming more crucial than ever. Recent work has demonstrated the use of deep learning techniques to improve prefetching accuracy, albeit with impractical compute and storage overheads. This paper suggests taking inspiration from the learning mechanisms and memory architecture of the human brain---specifically, the hippocampus and neocortex---to build resource-efficient, accurate, and adaptable prefetchers.