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
Read moreAccelerated 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.
Read moreContainerized 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.
Read moreCPU, 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.
Read moreHow 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.
Read moreAccelerating 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.
Read moreInAccel 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.
Read morePress Release
Athens, Greece (July 5, 2018) - InAccel Inc. (https://www.inaccel.com/), provider of FPGA accelerators on the cloud, announces today the completion of a $600,000 seed investment round by Marathon Venture Capital (https://marathon.vc/). InAccel will use the investment to accelerate product development and distribution of its flagship products across public cloud vendors.
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