Accelerated Machine Learning



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Accelerated ML Suite on AWS


Machine learning accelerators providing all the required APIs and libraries for the seamless integration in scalable distributed systems (C/C++, Java, Python Scala, R, Spark ML and Mahout)


Accelerated Machine Learning Suite


InAccel provides the accelerated machine learning suite in two versions:
  • Single-node Machine learning accelerators providing APIs for C/C++, Java, Python and Scala
  • Machine learning accelerators for Apache Spark providing all the required APIs and libraries for the seamless integration in distributed systems

InAccel's Accelerated Machine Learning Suite (AML) is a fully integrated framework that includes both the Software APIs/libraries and the FPGA files for accelerating your machine learning applications. 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 of machine learning models.
The accelerators can achieve up to 10x 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.
Logistic regression is used for building predictive models for many complex pattern-matching and classification problems. It is used widely in such diverse areas as bioinformatics, finance and data analytics. It is also one of the most popular machine learning techniques. It belongs to the family of classifiers known as the exponential or log-linear classifiers and is widely used to predict a binary response.

The specific IP core implements the (Batch) Gradient Descent algorithm for the Logistic Regression. For more information on the Logistic Regression cores check the datasheet: LogisticRegression Datasheet

K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem and is applicable in a variety of disciplines, such as computer vision, biology, and economics. It attempts to group individuals in a population together by similarity, but not driven by a specific purpose. The procedure follows a simple and easy way to cluster the training data points into a predefined number of clusters (K). The main idea is to define K centroids c, one for each cluster.​

Logistic regression


2.5x faster execution of logistic regression on MNIST (24GB) compared to 48-core processors

K-means clustering


2.5x faster execution time for K-means clustering on MNIST (24GB) compared to high-performance 48-core processor.

Cost reduction


2.5x lower TCO compared with 48-core processors. The TCO to run MNIST drops from $3 to $1.4 for Kmeans clustering and $1.2 for logistic regression.

Accelerated Spark ML on aws


Download the Solution brief for Spark ML suite on amazon aws.