Collective Knowledge Aggregator
proof-of-concept
Crowd results
Raw CK browser
Graphs
Reports
Datasets
Models
Home
Select CK-powered unified experimental workflow:
crowd-benchmark DNN libraries and models using mobile devices
crowd-benchmark DNN libraries and models
open ReQuEST @ ASPLOS'18 tournament (Pareto-efficient image classification)
crowd-benchmark DNN libraries and models (Caffe - dev)
crowd-benchmark DNN libraries and models (Caffe2)
crowd-benchmark DNN libraries and models (TensorFlow)
crowd-benchmark DNN libraries and models (dividiti desktop app)
auto/crowd-tune GCC compiler flags (minimize execution time)
auto/crowd-tune LLVM compiler flags (minimize execution time)
auto/crowd-tune GCC compiler flags (minimize execution time, do not degrade code size)
auto/crowd-tune GCC compiler flags (minimize total binary size, do not degrade execution time)
auto/crowd-tune LLVM compiler flags (minimize execution time, do not degrade code size)
auto/crowd-tune OpenCL-based CLBlast (GFLOPs)
crowd-test OpenGL compilers (beta)
crowd-test OpenCL compilers (beta) - crowdsource bug detection via CK
crowd-benchmark shared workloads via ARM WA framework
(under development) auto/crowd-tune CUDA work size (execution time)
(under development) auto/crowd-tune OpenCL local work size (execution time)
(under development) auto/crowd-tune OpenCL local work size (execution time/FPS vs energy)
(under development) crowdsource program scalability
(under development) crowdsource program numerical stability
(under development) crowdsource OpenCL bug detection
(under development) crowdsource modeling of program behavior
auto/crowd-tune GCC compiler flags (custom dimensions)
[ Participated
users
,
platforms
,
OS
,
CPU
,
GPU
,
GPGPU
,
NN
,
NPU
] [
How to participate
] [ Motivation (
PPT
) (
PDF
) ] [ Papers
1
,
2
,
3
] [
Android app
] [
Collective training set
] [
Unified AI
]
[
Community-driven AI R&D powered by CK
], [
CK-Caffe2
/
CK-Caffe
], [
CK-TensorFlow
], [
Wikipedia
,
paper 1
,
Paper 2
,
YouTube CK intro
], [
CGO'17 test of time award for our interdisiplinary R&D
]
Type:
cpu
cuda
opencl
DNN engine:
BVLC Caffe framework (cpu)
BVLC Caffe framework (cuda)
BVLC Caffe framework (cudnn)
BVLC Caffe framework (opencl,clblast)
BVLC Caffe framework (opencl,libdnn,clblast)
BVLC Caffe framework (opencl,libdnn,viennacl)
BVLC Caffe framework (opencl,viennacl)
Model:
bvlc, alexnet
bvlc, googlenet
deepscale, squeezenet, 1.0
deepscale, squeezenet, 1.1
resnet, resnet101
resnet, resnet152
resnet, resnet50
tidsp, jacintonet11, non-sparse
Platform:
Acer V1.06 (Aspire ES1-520)
GIGABYTE 0100 (H270-T70)