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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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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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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InAccel at XDF 2018

Inaccel presented the new version of the scalable accelerators for Apache Spark on October 2nd at Xilinx's premier event XDF in San Jose. The new version of the ML accelerators for Apache Spark are scalable to a high number of FPGA nodes without and it is fully integrated with Spark. The new version of the accelerators in the cloud allows up to 3x speedup and 2x lower OpEx compared to contemporary processors.

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Press Release

Athens, Greece (July 5, 2018) - InAccel Inc. (, provider of FPGA accelerators on the cloud, announces today the completion of a $600,000 seed investment round by Marathon Venture Capital ( InAccel will use the investment to accelerate product development and distribution of its flagship products across public cloud vendors.

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