Lune

MICRO2020顶会

FReaC Cache: Folded-logic Reconfigurable Computing in the Last Level Cache

Ashutosh Dhar, Xiaohao Wang, Hubertus Franke, Jinjun Xiong, Jian Huang, Wen-Mei W. Hwu, Nam Sung Kim, Deming Chen

2020年份
4被引次数
1顶会引用

摘要

The need for higher energy efficiency has resulted in the proliferation of accelerators across platforms, with custom and reconfigurable accelerators adopted in both edge devices and cloud servers. However, existing solutions fall short in providing accelerators with low-latency, high-bandwidth access to the working set and suffer from the high latency and energy cost of data transfers. Such costs can severely limit the smallest granularity of the tasks that can be accelerated and thus the applicability of the accelerators. In this work, we present FReaC Cache, a novel architecture that natively supports reconfigurable computing in the last level cache (LLC), thereby giving energy-efficient accelerators low-latency, high-bandwidth access to the working set. By leveraging the cache's existing dense memory arrays, buses, and logic folding, we construct a reconfigurable fabric in the LLC with minimal changes to the system, processor, cache, and memory architecture. FReaC Cache is a low-latency, low-cost, and low-power alternative to off-die/offchip accelerators, and a flexible, and low-cost alternative to fixed function accelerators. We demonstrate an average speedup of 3X and Perf/W improvements of 6.1X over an edge-class multi-core CPU, and add 3.5% to 15.3% area overhead per cache slice.

问问这篇 Paper

智能体会读完全文。

Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。

可以从这些问题问起

智能体调用

Luneget_paper_fulltext

在 Lune 里问

免费开始,无需绑卡

引用它的顶会 Paper1

问问它们各自怎么用它

相关 Paper

黄昏的海面,两侧是细线勾勒的悬崖