Accelerated Machine Learning

Speedup your application from your browser

using the power of accelerators

Speed up your applications
from your browser

InAccel’s Accelerated Machine Learning Suite (AML) is a fully integrated framework that allows to speedup your application from framework like C/C++, Python, Scala, Jupyter notebooks and Spark wth zero code changes.  

It aims to maintain the practical and easy to use interface of other open-source frameworks and at the same time to accelerate the training part or the classification of machine learning models. 

The accelerators can achieve up to 15x speedup compared to multi-threaded high performance processors. InAccel provides all the required APIs in Python, Scala and Java for the seamless integration of the accelerators in your applications.

InAccel Accelerated Machine Learning suite

Accelerated ML models

InAccel’s Accelerated ML suite can be used to speedup significantly widely-used machine learning applications like logistic regression and K-means clustering.

Also it allows sharing of resources from multiple users and scalable deployment to multiple cards.

Available Accelerators:

  • Deep Neural networks - Inference (Resnet50)
  • Logistic regression
  • K-means clustering
  • Naive Bayes
  • XGboost
Logistic regression acceleration using InAccel
Logistic regression cost on AWS using InAccel FPGAs

Save cost from faster execution

The speedup you achieve using InAccel’s ML suite comes also with a significant reduction on the operational expenses. While the accelerators cost higher (per hour) compared to typical processors, when you take into account the reduction of the total execution time, you can achieve up to 2.6 reduction on the operational expenses (TCO)

Faster execution time from Jupyter Notebooks

Accelerated ML on Jupyter notebook Using InAccel Accelerated ML suite you can speedup you ML applications instantly on Jupyter notebooks. 

Check the video on how you can speedup your ML applications just with a click of a button on Jupyter notebooks.

Same tools. Faster execution

Use the same notebooks. 

Just import the inaccel library and enjoy up to 15x faster execution time for your application. 

Speedup the hyper-parameter tuning, the training or even the classification using your familiar tools. 


1. Log in

Log in securely using your Google account.

2. Select the application

Select the application that you need to speedup with ready to use examples

  • Machine Learning (InAccel)
  • DNN - Inference (ResNet50)
  • Quantitative Finance (Vitis)
  • Compression (Vitis)
  • Encryption (Vitis)
  • Vision (Vitis)
  • Genomics

3. Run on your browser

Run your application from:

  • Terminal
  • Python
  • Jupyter

Get instant acceleration just by invoking the function.

Note: The online web platform is available for demonstration purposes to show the easy of deployment using FPGA-based accelerators. Multiple users may share the available resources which may affect the performance of the applications. If you want to have exclusive access to speedup your applications contact us at