Introducing

Deep Sparse

Optimization Tools & CPU Inference Engine


Sparsify and quantize your deep learning models to minimize footprint & run on CPUs at GPU speeds.
YOLOv3 | Batch = 64 | AWS c5.12xlarge CPU | Details
YOLOv3 | Batch = 64 | AWS c5.12xlarge CPU | Details

Benefits


Unprecedented Performance –– Run models on CPUs at GPU speeds. No special hardware required.
Reduce Costs –– Deploy and scale models on commodity CPU servers from the cloud to the edge.
Smaller Footprint –– Unlock edge possibilities by reducing model footprint by 20x.
Run Anywhere –– Deploy with flexibility on premise, in the cloud, or at the edge.

ComponentsCommunity Edition


Sparsify
Open-source, easy-to-use interface to automatically sparsify and quantize deep learning models for CPUs & GPUs.
SparseML
Open-source libraries and optimization algorithms for CPUs & GPUs, enabling integration with a few lines of code.
SparseZoo
Open-source neural network model repository for highly sparse and sparse-quantized models with matching pruning recipes for CPUs and GPUs.
DeepSparse Engine
Free CPU runtime that runs sparse models at GPU speeds.

Paths to Sparse Acceleration


path-to-acceleration Sparse Model Sparse Model DeepSparse Engine SparseZoo Transfer Learning SparseML Sparsify Your Dense Library Sparse Model Dense Model Sparse Model Dense Model DeepSparse Engine Your Training Process Your Dense Library Dense Model Dense Model DeepSparse Engine Your Training Process
A.) Original Dense Path
Take your dense model & run it in the DeepSparse Engine, without any changes.
B.) SparseZoo Path
Take a pre-optimized model & run it in the DeepSparse Engine, or transfer learn with your data.
C.) Sparsified Path
Sparsify and quantize your dense model with ease & run it in the DeepSparse Engine.
On-Demand Video: Learn About the Deep Sparse Platform
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