Machine learning algorithms are extremely computationally intensive and time consuming when they must be trained on large amounts of data. Typical processors are not optimized for machine learning applications and therefore offer limited performance. Therefore, both academia an industry is focused on the development of specialized architectures for the efficient acceleration of machine learning applications.
FPGAs are programmable chips that can be configured with tailored-made architectures optimized for specific applications. As FPGAs are optimized for specific tasks, they offer higher performance and lower energy consumption compared with general purpose CPUs or GPUs. FPGAs are widely used in applications like image processing, telecommunications, networking, automotive and machine learning applications.
Recently major cloud and HPC providers like Amazon, Alibaba, Huawei and Nimbix have started deploying FPGAs in their data centers. However, currently there are limited cases of wide utilization of FPGAs in the domain of machine learning.
Towards this end, InAccel has released today as open-source the FPGA IP core for the training of logistic regression algorithms. The accelerated FPGA IP core offers up to 70x speedup compared to a single threaded execution and up to 12x compared to an 8-core general purpose CPU execution respectively.
The IP core for logistic regression leverage the processing power of the Xilinx FPGAs. The IP core is optimized for the Xilinx FPGAs like Alveo U200 and U250 cards and the FPGAs available as instances on the cloud providers (f1 on AWS and f3 on Alibaba cloud).
The release of the Logistic Regression IP core will help demonstrate the advantages of the FPGAs in the domain of machine learning and it will offer to the data science community the chance to experiment, deploy and utilize FPGAs in order to speedup their machine learning applications.
The logistic regression IP core can be used as an add-on library that overload the functions for the logistic regression training. InAccel offers all the required APIs for seamless integration with Python, Java and Scala. That means that data scientist and data engineers do not need to change their code at all.
The IP core is available on https://github.com/inaccel/logisticregression