Blog

13May

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.

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10Apr

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.

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01Apr

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.

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20Mar

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.

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28Jan

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

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03Jan

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.

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27Dec

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.

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17Dec

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.

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26Nov

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.

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26Oct

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.

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