TECHNOLOGY
DEEPCUBE’S TECHNOLOGY IN A NUTSHELL
DeepCube focuses on research and development of deep learning technologies that result in improved real-world deployment of AI systems. The company’s numerous patented innovations include methods for faster and more accurate training of deep learning models, and drastically improved inference performance.
DeepCube’s proprietary framework can be deployed on top of any existing hardware in both datacenters and edge devices, resulting in over 10x speed improvement and memory reduction. DeepCube provides the only technology that allows efficient deployment of deep learning models on intelligent edge devices.
DEEPCUBE’S TECHNOLOGY IN A NUTSHELL
DeepCube focuses on research and development of deep learning technologies that result in improved real-world deployment of AI systems. The company’s numerous patented innovations include methods for faster and more accurate training of deep learning models, and drastically improved inference performance.
DeepCube’s proprietary framework can be deployed on top of any existing hardware in both datacenters and edge devices, resulting in over 10x speed improvement and memory reduction. DeepCube provides the only technology that allows efficient deployment of deep learning models on intelligent edge devices.
DEEPCUBE’S TECHNOLOGY IN A NUTSHELL
DeepCube focuses on research and development of deep learning technologies that result in improved real-world deployment of AI systems. The company’s numerous patented innovations include methods for faster and more accurate training of deep learning models, and drastically improved inference performance.
DeepCube’s proprietary framework can be deployed on top of any existing hardware in both datacenters and edge devices, resulting in over 10x speed improvement and memory reduction. DeepCube provides the only technology that allows efficient deployment of deep learning models on intelligent edge devices.
PROBLEM: DEEP LEARNING DEPLOYMENTS REMAIN VERY LIMITED
LATENCY AND MEMORY
DEDICATED HARDWARE
OPTIMIZED FOR CLOUD
COSTS OF CLOUD INFERENCE
LATENCY AND MEMORY
DEDICATED HARDWARE
LATENCY AND MEMORY
DEDICATED HARDWARE
DEEPCUBE’S PILLARS
SPARSIFICATION
Agnostic to model type (MLP, CNN, RNN) that results in a significantly more lightweight model (represented as a sparse graph instead of dense tensor).
PROPRIETARY INFERENCE MODEL
DeepCube developed its proprietary techniques and methods from scratch, which are highly optimized for running sparse deep learning models for inference, and result in dramatic speedup and memory reduction on any existing hardware.
FULLY AUTOMATED
DeepCube’s platform reduces the size of any deep learning model (any training data and model type) in a completely automated way and without any manual intervention.
SPARSIFICATION
Agnostic to model type (MLP, CNN, RNN) that results in a significantly more lightweight model (represented as a sparse graph instead of dense tensor).
PROPRIETARY INFERENCE MODEL
DeepCube developed a proprietary techniques and methods from scratch, highly optimized for running sparse deep learning models for inference, resulting in dramatic speedup and memory reduction on any existing hardware.
FULLY AUTOMATED
DeepCube platform reduces the size of any deep learning model (any training data and model type) in a completely automated way without any manual intervention.
DEEP LEARNING WITH DEEPCUBE
Traditional Neural Network
Neural Network with DeepCube
~90% size reduction during training
Traditional Neural Network
Neural Network with DeepCube
~90% size reduction during training
DEEP LEARNING DEPLOYMENT ON ANY DEVICE
SPEED IMPROVEMENT & MEMORY REDUCTION
OS & HARDWARE AGNOSTIC
EFFICIENT DEPLOYMENT ON EDGE DEVICES
SPEED IMPROVEMENT & MEMORY REDUCTION
OS & HARDWARE AGNOSTIC
EFFICIENT DEPLOYMENT ON EDGE DEVICES
USE CASES
USE CASES
