{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stacked-filters-stationary-flow-for-hardware","title":"Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks","arxiv_id":"1801.07459","date":"2018-01-23","proceeding":null,"authors":["Yuechao Gao","Nianhong Liu","Sheng Zhang"],"abstract":"To address memory and computation resource limitations for hardware-oriented\nacceleration of deep convolutional neural networks (CNNs), we present a\ncomputation flow, stacked filters stationary flow (SFS), and a corresponding\ndata encoding format, relative indexed compressed sparse filter format (CSF),\nto make the best of data sparsity, and simplify data handling at execution\ntime. And we also propose a three dimensional Single Instruction Multiple Data\n(3D-SIMD) processor architecture to illustrate how to accelerate deep CNNs by\ntaking advantage of SFS flow and CSF format. Comparing with the\nstate-of-the-art result (Han et al., 2016b), our methods achieve 1.11x\nimprovement in reducing the storage required by AlexNet, and 1.09x improvement\nin reducing the storage required by SqueezeNet, without loss of accuracy on the\nImageNet dataset. Moreover, using these approaches, chip area for logics\nhandling irregular sparse data access can be saved. Comparing with the 2D-SIMD\nprocessor structures in DVAS, ENVISION, etc., our methods achieve about 3.65x\nprocessing element (PE) array utilization rate improvement (from 26.4\\% to\n96.5\\%) on the data from Deep Compression on AlexNet.","url_abs":"http://arxiv.org/abs/1801.07459v3","url_pdf":"http://arxiv.org/pdf/1801.07459v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stacked-filters-stationary-flow-for-hardware","repo_url":"https://github.com/songhan/Deep-Compression-AlexNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"caffe2","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"fire-module","method_name":"Fire Module"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"squeezenet","method_name":"SqueezeNet"},{"method_slug":"xavier-initialization","method_name":"Xavier Initialization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}