InAccel’s Accelerated Machine Learning Studio (AML) is a fully integrated framework that allows to speedup your C/C++, Python, Java and Scala applications with 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 or the classification part 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, C/C++ and Java for the seamless integration of the accelerators with your applications.
InAccel studio is a managed cloud platform for data scientists and software developers. We provide the tools to utilize the power of FPGA accelerators in the most efficient way.
Check this 1-min video to learn how InAccel can help you speedup your ML and DL applications with zero code changes.
Enjoy 10x-20x speedup on your applications utilizing the power of accelerators stress-free.
Using familiar frameworks like Jupyter, Scikit Learn and Keras, Inaccel instantly offloads the most computationally intensive functions to the hardware accelerators.
Pay-as-you-go: Pay only for the days that you are using the InAccel studio in a secure cloud-based environment (using aws f1 instances).
InAccel’s Accelerated ML suite can be used to significantly speedup widely-used machine learning applications like logistic regression and K-means clustering.
It also allows the available resources to be shared across multiple users as well as scalable deployments to multiple FPGA cards.
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)
Speedup your ML applications instantly on Jupyter notebooks using InAccel’s Accelerated ML suite.
Check the video on how you can speedup your ML applications just with a click of a button on Jupyter notebooks.
Same notebook structure.
Just import the inaccel library and enjoy up to 15x faster execution time for your applications.
Speedup the hyper-parameter tuning, the training or even the classification with widely-used state-of-the-art tools.
Inference also supported (i.e. ResNet50) with high frame rates of up to 3,000 fps per FPGA card.
Select the application that you need to speedup with ready to use examples
Note: The online web platform is available for demonstration purposes to promote the ease of deployment using FPGA-based accelerators. Multiple users may share the available resources which could affect the performance of the applications. If you want to have exclusive access to speedup your applications contact us at firstname.lastname@example.org.