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Run 100x faster your Scikit-learn machine learning applications using FPGAs: A use case on Naive Bayes

Check how you can speedup your Scikit learn applications by more than 100x using FPGAs and with zero code changes.

How to make FPGA deployment in data centers as easy as any computing resource

, InAccel developed the unique FPGA orchestrator that allows easy deployment, instant scaling and seamless resource management of the FPGA clusters. InAccel’s FPGA orchestrator allows the deployment of FPGAs as a Platform ready to be used by the software developers just like any other computing system. At the same time, InAccel’s orchestrator integrated with InAccel’s FPGA repository allows the utilization of FPGAs as a service using off-the-shelf ready to use accelerators.

Multi-tenant, enterprise-grade FPGA cluster deployment using Kubesphere console

Kubernetes on FPGA using Kubesphere extends the industry standard container orchestration platform with FPGA acceleration capabilities. With first class support for FPGA resources scheduling, developers and DevOps engineers can now build, deploy, orchestrate and monitor FPGA-accelerated application deployments on heterogeneous, multi-cloud clusters.

How FPGAs can prevail as the next computing platform

One of the main issues raised by many participants and speakers was the need for an OS layers for the widespread adoption of FPGAs that will allow easy deployment, instant scaling and seamless sharing of the FPGA resources. An abstraction layer that will allow software developers to utilize FPGAs without prior knowledge of FPGA can accelerate the adoption of FPGAs in many applications.

Making FPGAs deployment as easy as processors

CPU, GPU or FPGA: Performance evaluation of cloud computing platforms for Machine Learning training

FPGAs on the cloud (f1.2xlarge on this case with InAccel ML suite) achieves the best combination in terms of performance-accuracy and cost.

Deploying FPGAs from Git repos instantly using BinderHub

Connecting 2 worlds: Bringing the benefits of accelerators to the software community, instantly

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

25Feb Run 100x faster your Scikit-learn machine learning applications using FPGAs: A use case on Naive Bayes Check how you can speedup your Scikit learn applications by more than 100x using FPGAs and with zero code changes. Read more 18Feb How to make FPGA deployment in data centers as easy as any computing resource , […]

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.