Reduce neural network computing power requirements without sacrificing performance and accuracy
Introducing enot.ai

Benchmarks

Benchmarks for some of the most popular open-source neural networks models after they have been optimized with enot.ai’s tech.
View more in our benchmarks section.

Object detection

Yolo_v5s

6.8x

acceleration

Object detection

MobileNet_v2_SSD-lite

12.4x

acceleration

Image classification

MobileNet_v2

11.2x

acceleration

Image classification

Resnet50

11.2x

acceleration

NLP

BERT

9.3x

acceleration

Why enot.ai?
as our tools simplify and automate neural network deployment
Reduce AI development costs & time
for battery-constrained devices & machines
Reduce power consumption
without compromising accuracy
Deploy neural networks on edge devices
while reducing computing power requirements
Decrease cloud processing costs
while keeping the existing hardware
Increase speed & accuracy of neural networks
without sacrificing performance and accuracy
Reduce neural network computing power requirements
Get started by implementing only a few lines of code
Whatever your use case and neural network pipeline, enot.ai’s products are quick to set up and ready to use. Read more below to learn what is the fastest path towards compressing & accelerating your neural networks.
Enot.ai products
Enot Lite
Our custom-built Inference engine for quick neural network acceleration results


Perfect if: you use PyTorch/Tensorflow for inferencing on an Intel CPU/Nvidia GPU
Expected compression: 2-4x
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Enot Pro
Our proprietary Neural network architecture selection engine for compressing any custom models

Perfect if: you use PyTorch for neural network training, and are looking for the highest possible compression rates
Expected compression: up to 12x
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Enot Zoo
A collection of the most widely used models that have already been accelerated by enot.ai

Perfect if: you are looking for ready-to-use models that consume significantly less computing power, yet provide the same performance
Expected compression: 3-12x
View Database
Technology
During the process, enot.ai’s engine takes several parameters into account to achieve the highest compression/ acceleration rate without accuracy degradation:
Enot.ai’s (Neural Network Architecture Selection) technology allows to automatically choose a sub-network from an existing trained neural network that is faster but has the same accuracy.
In turn, enot.ai’s engine allows to automate the search for the optimal neural network architecture, taking into account latency, RAM and model size constraints for different hardware and software platforms.
input resolution
depth of neural network
number of filters at each layer
latency of each operation on target HW
News
Accelerate your neural networks today!
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