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Speedup your applications with zero code changes: The seamless way to make FPGA accessible to the software community

Accelerating data science and HPC applications with FPGAs using Jupyter Hub, instantly

InAccel, a world-leader in application acceleration through the use of adaptive acceleration platforms (ACAP, FPGA) has integrated JupyterHub in its technology. InAccel provides an FPGA resource manager that allows the instant deployment, scaling and virtualization of FPGAs making easier than ever the utilization of FPGA clusters for applications like machine learning, data processing, data analytics and many more HPC workloads.

Accelerating Spark ML over SQL Server 2019 Big Data Cluster, instantly

Automated deployment, scaling and management of FPGA clusters: the easy way

Coral is a scalable, reliable and fault-tolerant distributed acceleration system responsible for monitoring, virtualizing and orchestrating clusters of FPGAs. Coral also introduces high-level abstractions by exposing FPGAs as a single pool of accelerators to any application developer that she can easily invoke through simple API calls. Finally, Coral runs as a microservice and is able to run on top of other state-of-the-art resource managers like Hadoop YARN and Kubernetes.

InAccel’s Accelerated ML suite boosts Spark ML as much as 7x using Intel’s® Arria® FPGAs

The IP cores for logistic regression and K-means clustering leverage the processing power of the Intel FPGAs to speedup the training of these algorithms. The IP core is optimized for the Intel® FPGAs (e.g. Arria® 10) available as instances on Alibaba cloud.

InAccel Accelerates XGboost and releases the IP core for FPGAs

InAccel has released today as open-source the FPGA IP core for the training of XGboost. The FPGA accelerated solution for the XGBoost algorithm is based on the Exact (Greedy) algorithm for tree creation. It can provide up to 26x speedup compared to a single threaded execution and up to 5x compared to an 8 threaded CPU execution respectively

InAccel releases world’s first universal bitstream repository for FPGAs based on JFrog

InAccel, a company specialized on FPGA accelerators, developed world’s first bitstream repository for FPGAs based on the JFrog artifactory.

InAccel releases open-source Logistic Regression IP core for FPGAs

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.

InAccel releases world's first FPGA orchestrator

InAccel, a start-up company specialized on accelerators for machine learning, has released today the latest version of the Coral FPGA resource manager that allows the software community to instantiate and utilize a cluster of FPGAs with the same easy as invoking typical software functions. InAccel’s Coral FPGA resource manager allows multiple applications to share and utilize a cluster of FPGAs in the same node (server) without worrying about the scheduling, load balancing and the resource management of each FPGA.

FPGAs goes serverless on kubernetes

14Nov Speedup your applications with zero code changes: The seamless way to make FPGA accessible to the software community Read more 11Nov Accelerating data science and HPC applications with FPGAs using Jupyter Hub, instantly InAccel, a world-leader in application acceleration through the use of adaptive acceleration platforms (ACAP, FPGA) has integrated JupyterHub in its technology. […]

How to save over $700k on your next machine learning project

How to save over $700k on your next machine learning project using FPGA-based hardware accelerators and without changing your code at all.

Accelerated Spark ML on top Microsoft SQL Server 2019 Big Data Cluster

Check how you can run 15x faster your Spark ML applications connected to Microsoft SQL server 2019.

FPGA meets Apache Arrow

In this article, we introduce a novel framework that allows the seamless integration of FPGAs under Apache Arrow development platform. The integration of FPGA with Apache Arrow-compatible frameworks allows the acceleration of data science applications without any prior experience on FPGAs.

InAccel releases Coral Resource Manager for seamless deployment of FPGA clusters

InAccel today released the new version of the Coral FPGA resource manager that allow FPGA users to seamlessly deploy and manage FPGA cluster on the cloud or on-premise.

Accelerate ML training on AWS and Reduce the TCO

Check how you can use the new f1 Accelerators on AWS to speedup and reduce the TCO of Machine Learning training

Accelerated Machine Learning training with a push of a button

InAccel, a world-leader in application acceleration, has released today the new Accelerated Machine Learning suite through the Nimbix Cloud infrastructure. The Nimbix Cloud offers both enterprise software users and application developers a platform for accelerated computing for next-generation datacenter applications.

Containerized FPGA Manager for Seamless Application Acceleration and Infrastructure Scalability

InAccel exploits the high computational efficiency of FPGAs to deliver application acceleration services that provides up to 10x faster execution along with 3x cost reduction. Our belief as well as fundamental design focus is that acceleration must be delivered effortless to the user. To this end, InAccel offers seamless infrastructure as well as application integration.

CPU, GPU, FPGA or TPU: Which one to choose for my Machine Learning training?

Currently, cloud providers offer a plethora of choices when it comes to the processing platform that will be used to train your machine learning application. AWS, Alibaba cloud, Azure and Huawei offers several platforms such as general purpose CPUs, compute-optimized CPUs, memory-optimized CPUs, GPUs, FPGAs and Tensor Flow Processing Units.

How to train your ML model 3x faster without changing your code

Training a ML model can take a lot of time especially when you have to process huge amounts of data. Typical general-purpose processors (CPUs) or GPUs are designed to be flexible but are not very efficient on machine learning training. In the domain on embedded systems, that problem was solved many years ago by using specialized chips that are designed for specific applications (i.e. FPGAs). FPGAs are programmable chips that can be configured with specialized architectures. In the FPGAs, instructions that needs to process the data are hard-wired in the chip. Therefore, they can achieve much better performance than CPUs and consume much lower power.

Accelerating Data Science

Emerging cloud applications like machine learning, AI and big data analytics require high performance computing systems that can sustain the increased amount of data processing without consuming excessive power. Towards this end, many cloud operators have started adopting heterogeneous infrastructures deploying hardware accelerators, like FPGAs, to increase the performance of computational intensive tasks. However, most hardware accelerators lack of programming efficiency as they are programmed using not-so widely used languages like OpenCL, VHDL and HLS.