From f194663629398786c75ce8ad0d278f9cde650ce8 Mon Sep 17 00:00:00 2001 From: Benjamin Fineran Date: Fri, 26 Feb 2021 15:06:56 -0500 Subject: [PATCH] Revert "Rm: blog link, 7x (#54)" (#57) This reverts commit a83e83988f4495713eecaf0b22b0026fc0df0eab. --- README.md | 2 +- docs/source/index.rst | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index a63c937031..c0800485a5 100644 --- a/README.md +++ b/README.md @@ -56,7 +56,7 @@ This repository includes package APIs along with examples to quickly get started Sparsification is the process of taking a trained deep learning model and removing redundant information from the overprecise and over-parameterized network resulting in a faster and smaller model. Techniques for sparsification are all encompassing including everything from inducing sparsity using [pruning](https://neuralmagic.com/blog/pruning-overview/) and [quantization](https://arxiv.org/abs/1609.07061) to enabling naturally occurring sparsity using [activation sparsity](http://proceedings.mlr.press/v119/kurtz20a.html) or [winograd/FFT](https://arxiv.org/abs/1509.09308). When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics. -For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy. +For example, pruning plus quantization can give over [7x improvements in performance](https://neuralmagic.com/blog/benchmark-resnet50-with-deepsparse) while recovering to nearly the same baseline accuracy. The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches. Recipes encode the directions for how to sparsify a model into a simple, easily editable format. diff --git a/docs/source/index.rst b/docs/source/index.rst index ef78b48a2a..0a2344b29b 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -62,7 +62,7 @@ Sparsification Sparsification is the process of taking a trained deep learning model and removing redundant information from the overprecise and over-parameterized network resulting in a faster and smaller model. Techniques for sparsification are all encompassing including everything from inducing sparsity using `pruning `_ and `quantization `_ to enabling naturally occurring sparsity using `activation sparsity `_ or `winograd/FFT `_. When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics. -For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy. +For example, pruning plus quantization can give over `7x improvements in performance `_ while recovering to nearly the same baseline accuracy. The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches. Recipes encode the directions for how to sparsify a model into a simple, easily editable format. @@ -131,4 +131,4 @@ Additionally, more information can be found via Bugs, Feature Requests Support, General Q&A - Neural Magic Docs + Neural Magic Docs \ No newline at end of file