第31场概述:机器学习的内存计算:技术方向和内存小组委员会

N. Verma, F. Hamzaoglu, M. Nagata, Leland Chang
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引用次数: 0

摘要

许多最先进的机器学习系统在所需的能量和可以实现的性能方面受到内存的限制。本节将探讨如何通过在内存数组内执行计算的新兴架构来克服这一瓶颈。这就需要非常规的、典型的混合信号计算电路,它利用机器学习应用的统计特性,以大量的能量和吞吐量增益来实现高算法性能。此外,这些架构还可以作为新兴内存技术的驱动力,利用这些技术提供的高密度和非易失性来提高计算的规模和效率。本次会议上的创新论文通过超越传统架构,提供了这一承诺的具体演示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Session 31 overview: Computation in memory for machine learning: Technology directions and memory subcommittees
Many state-of-the-art systems for machine learning are limited by memory in terms of the energy they require and the performance they can achieve. This session explores how this bottleneck can be overcome by emerging architectures that perform computation inside the memory array. This necessitates unconventional, typically mixed-signal, circuits for computation, which exploit the statistical nature of machine-learning applications to achieve high algorithmic performance with substantial energy and throughput gains. Further, the architectures serve as a driver for emerging memory technologies, exploiting the high-density and nonvolatility these offer towards increased scale and efficiency of computation. The innovative papers in this session provide concrete demonstrations of this promise, by going beyond conventional architectures.
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