Use Case: Data Centers

Deep Learning Challenges Within Data Centers

After a deep learning model is trained, the resulting model typically consumes a great deal of memory and requires expensive and dedicated hardware (e.g. GPUs) for deployment (inference mode). Consequently, deep learning deployments today are limited primarily to the cloud, and even in these cases, they incur extensive processing costs, significant memory requirements, and expensive power costs, due to intensive computing demands.

DeepCube - Data Centers Challenges
DeepCube - Data Centers Challenges

Data Centers Deep Learning Challenges

After a deep learning model is trained, the resulting model typically consumes lots of memory and requires dedicated hardware (e.g. GPUs) for deployment (inference mode). Consequently, deep learning deployment today is limited mostly to cloud, and even there, involves huge costs for expensive processors, large amounts of memory, and especially high electricity costs, due to intensive computing requirements.

DeepCube - Large Scale

DeepCube Applies to Large Scale AI Deployments Across Industries

DeepCube’s inference accelerator that enables drastic speed increases on any existing hardware is optimized for large enterprises with huge databases, heavy computing processes, data crunching, workloads, etc. This covers the entire AI deployment market, in any sector or industry, including, but not limited to:

  • Retail
  • Financial Institutions
  • Healthcare
  • Government
DeepCube - Large Scale

DeepCube Applies to Large Scale AI Deployments Across Industries

DeepCube’s inference accelerator, resulting in a drastic improvement of deep learning speed on any existing hardware, is optimized for large enterprises (huge databases, heavy computing processes, data crunching, workloads, etc.). This covers the entire AI deployment market, in any sector or industry, including:

  • Retail
  • Financial Institutions
  • Healthcare
  • Government
  • And more

Significant Workload Improvement on Top of Any Hardware

DeepCube’s proprietary framework can be deployed on top of any existing hardware and results in:

  • Major speed improvements – average rate is 10x
  • Cost savings – no hardware upgrade is required, just a simple software upgrade is necessary to obtain enhanced levels of performance
  • Major reduction in memory requirements
  • Substantial reduction in power consumption
DeepCube - Speed Improvement
DeepCube - Speed Improvement

Significant Workload Improvement on Top of Any Hardware

DeepCube’s proprietary framework can be deployed on top of any existing hardware and results in over 10x speed improvement and memory reduction:

  • Major speed improvement for any existing hardware (even the newest)
  • No hardware upgrade needed – costs reduction by performing a simple software upgrade in order to obtain the same (or oven better) performance level
  • Major reduction in memory requirements
  • Substantial reduction in power consumption

Data Centers Companies, Transform Your Business

With DeepCube, you can see massive gains in performance