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From edwardy...@apache.org
Subject incubator-horn git commit: Remove some comparison info.
Date Mon, 11 Apr 2016 08:26:25 GMT
Repository: incubator-horn
Updated Branches:
  refs/heads/master 9d3bf25d3 -> 4069d8686


Remove some comparison info.

Because, it's not a facts based on enough experiments.

Project: http://git-wip-us.apache.org/repos/asf/incubator-horn/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-horn/commit/4069d868
Tree: http://git-wip-us.apache.org/repos/asf/incubator-horn/tree/4069d868
Diff: http://git-wip-us.apache.org/repos/asf/incubator-horn/diff/4069d868

Branch: refs/heads/master
Commit: 4069d868615566baac26a24f38432c16af03affa
Parents: 9d3bf25
Author: Edward J. Yoon <edwardyoon@apache.org>
Authored: Mon Apr 11 17:25:19 2016 +0900
Committer: Edward J. Yoon <edwardyoon@apache.org>
Committed: Mon Apr 11 17:25:19 2016 +0900

----------------------------------------------------------------------
 README.md | 14 --------------
 1 file changed, 14 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/incubator-horn/blob/4069d868/README.md
----------------------------------------------------------------------
diff --git a/README.md b/README.md
index fd3ca9b..060dc8c 100644
--- a/README.md
+++ b/README.md
@@ -2,20 +2,6 @@
 
 The Apache Horn is an Apache Incubating project, a neuron-centric programming model and Sync
and Async hybrid distributed training framework, supports both data and model parallelism
for training large models with massive datasets. Unlike most systems having matrix approach
to neural network training, Horn adopted the the neuron-centric model which enables training
large-scale deep learning on highly scalable CPU cluster. In the future, we plan also to support
GPU accelerations for heterogeneous devices.
 
-## Tensor vs. Neuron
-
-While tensor-based models would require an large memory consumption or parallel computational
complexity to calibrate a large number of model parameters, the neuron-centric model has advantages
like below:
- 
- * More intuitive programming APIs
- * An effective partition and parallelization strategy for large model
- * Easy to understand how groups of neurons communicate 
-
-|             | Tensor           | Neuron  |
-| ------------- |:-------------:|:-----:|
-| Computation model	| tensor/matrix-based computation model | neuron-based iterative computation
model |
-| Partitioning models | Vector or Submatrix (block) | Subgraph components (densely connected
areas) |
-| Communication overhead | Large |  Small |
-
 ## High Scalability
 
 The Apache Horn is an Sync and Async hybrid distributed training framework. Within single
BSP job, each task group works asynchronously using region barrier synchronization instead
of global barrier synchronization, and trains large-scale neural network model using assigned
data sets in synchronous way.


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