A comprehensive survey on trustworthy recommender systems
As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …
people make appropriate decisions in an effective and efficient way, by providing …
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …
reduce the size of neural networks by selectively pruning components. Similarly to their …
Spatten: Efficient sparse attention architecture with cascade token and head pruning
The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …
(NLP) applications, showing superior performance than convolutional and recurrent …
Sigma: A sparse and irregular gemm accelerator with flexible interconnects for dnn training
The advent of Deep Learning (DL) has radically transformed the computing industry across
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …
A modern primer on processing in memory
Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …
design choice goes directly against at least three key trends in computing that cause …
Matraptor: A sparse-sparse matrix multiplication accelerator based on row-wise product
Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in
numerous application domains such as data analytics, graph processing, and scientific …
numerous application domains such as data analytics, graph processing, and scientific …
[图书][B] Efficient processing of deep neural networks
This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …
Sparch: Efficient architecture for sparse matrix multiplication
Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various
engineering and scientific applications. However, inner product based SpGEMM introduces …
engineering and scientific applications. However, inner product based SpGEMM introduces …
I-GCN: A graph convolutional network accelerator with runtime locality enhancement through islandization
Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three
years. Compared with other deep learning modalities, high-performance hardware …
years. Compared with other deep learning modalities, high-performance hardware …
A systematic survey of general sparse matrix-matrix multiplication
General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …