RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference

2021-01-29 · Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, Gu-Yeon Wei

Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment. State-of-the-art models comprise large embedding tables that have billions of parameters requiring large memory capacities. Unfortunately, large and fast DRAM-based memories levy high infrastructure costs. Conventional SSD-based storage solutions offer an order of magnitude larger capacity, but have worse read latency and bandwidth, degrading inference performance. RecSSD is a near data processing based SSD memory system customized for neural recommendation inference that reduces end-to-end model inference latency by 2X compared to using COTS SSDs across eight industry-representative models.

📄 PDF Abstract BibTeX arXiv:2102.00075

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…

Non Maximum Suppression Non Maximum Suppression is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The…

SSD SSD is a single-stage object detection method that discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per…

Equivariant Networks for Pixelized Spheres

2021-06-12 · Mehran Shakerinava, Siamak Ravanbakhsh

Pixelizations of Platonic solids such as the cube and icosahedron have been widely used to represent spherical data, from climate records to Cosmic Microwave Background maps. Platonic solids have well-known global symmet…

Semantic Segmentation

Accelerating Point Cloud Ground Segmentation: From Mechanical to Solid-State Lidars

2024-08-19 · Xiao Zhang, Zhanhong Huang, Garcia Gonzalez Antony, Xinming Huang

In this study, we propose a novel parallel processing method for point cloud ground segmentation, aimed at the technology evolution from mechanical to solid-state Lidar (SSL). We first benchmark point-based, grid-based, …

Autonomous Vehicles CPU Segmentation

In-Place Updates of a Graph Index for Streaming Approximate Nearest Neighbor Search

2025-02-19 · Haike Xu, Magdalen Dobson Manohar, Philip A. Bernstein, Badrish Chandramouli 외

Indices for approximate nearest neighbor search (ANNS) are a basic component for information retrieval and widely used in database, search, recommendation and RAG systems. In these scenarios, documents or other objects a…

Information Retrieval RAG

High-level numerical simulations of noise in CCD and CMOS photosensors: review and tutorial

2014-12-11 · Mikhail Konnik, James Welsh

In many applications, such as development and testing of image processing algorithms, it is often necessary to simulate images containing realistic noise from solid-state photosensors. A high-level model of CCD and CMOS …

Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities

2017-12-01 · NeurIPS 2017 12 · Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stephane Mallat

We introduce a solid harmonic wavelet scattering representation, invariant to rigid motion and stable to deformations, for regression and classification of 2D and 3D signals. Solid harmonic wavelets are computed by mul…

General Classification regression