GIRAF: General purpose In-storage Resistive Associative Framework

05/24/2018
by   Leonid Yavits, et al.
0

GIRAF is an in-storage architecture and algorithm framework based on Resistive Content Addressable Memory (RCAM). GIRAF functions simultaneously as a storage and a massively parallel associative processor. GIRAF alleviates the bandwidth wall by connecting every memory bit to processing transistors and keeping computing inside the storage arrays, thus implementing in-data, rather than near-data, processing. We show that GIRAF outperforms a reference computer architecture with a bandwidth-limited external storage access on a variety of data intensive workloads. The performance of GIRAF Euclidean distance, dot product and histogram implementation, exceeds the attainable performance of a reference architecture by up to four orders of magnitude, depending on the dataset size. The performance of GIRAF SpMV exceeds the attainable performance of such reference architecture by more than two orders of magnitude.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

05/24/2018

PRINS: Resistive CAM Processing in Storage

Near-data in-storage processing research has been gaining momentum in re...
04/03/2021

Nova-LSM: A Distributed, Component-based LSM-tree Key-value Store

The cloud infrastructure motivates disaggregation of monolithic data sto...
06/12/2016

Application-Driven Near-Data Processing for Similarity Search

Similarity search is a key to a variety of applications including conten...
04/29/2020

Mainlining Databases: Supporting Fast Transactional Workloads on Universal Columnar Data File Formats

The proliferation of modern data processing tools has given rise to open...
03/25/2020

Next-Generation Information Technology Systems for Fast Detectors in Electron Microscop

The Gatan K2 IS direct electron detector (Gatan Inc., 2018), which was i...
04/20/2016

CLAASIC: a Cortex-Inspired Hardware Accelerator

This work explores the feasibility of specialized hardware implementing ...
12/20/2020

Learning to Localize Through Compressed Binary Maps

One of the main difficulties of scaling current localization systems to ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.