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In this tutorial, we will lay out the absolute easiest way to begin using FPGA resources in Kubernetes clusters. To really simplify things, we will describe the process for enabling FPGAs in terms of the Rancher user interface. The Rancher UI is simply a client to the Rancher RESTful APIs. You can use other clients to the APIs, such as Golang, Python and Terraform, in GitOps, DevOps and other automated solutions. We won’t delve into any of those here.

Fundamentally, the process is simple:

Getting Up and Running with Rancher and Available FPGA Resources-

Rancher…


Customized compute acceleration in the datacenter is key to the wider roll-out of applications based on deep neural network (DNN) inference.

A great article by Xilinx Research Labs shows how to maximize the performance and scalability of FPGA-based pipeline dataflow DNN inference accelerators (DFAs) automatically on computing infrastructures consisting of multi-die, network-connected FPGAs. Xilinx as developed Elastic-DF, a novel resource partitioning tool which integrates with the DNN compiler FINN and utilizes 100Gbps Ethernet FPGA infrastructure, to achieve low-latency model-parallel inference without host involvement. …


AMD’s (AMD) $35 billion deal to acquire Xilinx (XLNX) has been recently approved by shareholders of both chipmakers. However there are several cases in the domain of deep learning that GPUs are considered more powerful than FPGAs. Then, why AMD decided to acquire Xilinx for $35 billion instead of further advancing its own GPUs? Further investing and developing on GPUs would also help compete the rising NVIDIA especially in the domain of data center where NVIDIA seems to have very ambitious plans.

It is true that in many cases GPUs can provide much better performance for some applications. For the…


FPGAs have been emerged as a high-performance computing platform that can meet the demanding AI requirements in terms of throughput, latency and energy-efficiency. FPGA vendors like Xilinx, Intel and Achronix have developed great FPGA platforms for data center and edge applications.

However a main challenge on the domain of AI/ML is the easy of deployment. Data Scientists and ML engineers care not only for high performance and fast execution but also for easy of deployment at scale. Deploying trained neural networks in applications and services can be very challenging for infrastructure managers.

InAccel provides a unique FPGA cluster manager that…


MindsDB is an open-source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using SQL queries. For more flexibility MindsDB has developed the Lightwood framework. Lightwood has one main class, the Predictor, which is a modular construct that you can train and get predictions from. It is made out of 3 main building blocks (features, encoders, mixers) that you can configure, modify and expand as you wish.

However, in many cases the training or the prediction task for machine learning can be quite computational intensive. This means that the training or…


Deep neural networks, and AI in general, can offer tremendous advantages in many sectors like healthcare, finance, logistics, marketing, and research. However, all the AI models like deep learning, reinforcement learning, etc. are computationally intensive and require enormously powerful processing platforms. This is the reason that so many large chip vendors and startups have been focused on the development of powerful specialized processing systems specifically for AI algorithms.

Karl Freund, liken the explosion of several AI chip vendors to the Cambrian period with the rapid diversification of life forms, known as the Cambrian explosion. There are several vendors providing specialized…


Automatic Object Detection using machine learning is one of the most promising technologies in the domain of video classification and detection. Object detection in video is computationally intensive task that requires huge amount of processing power. Hardware accelerators, based on FPGAs, can provide the required processing power to increase the throughput of the application and at the same time to reduce significantly the latency.

InAccel, a world-pioneer in the domain of FPGA-based accelerators, has released today an integrated framework that allows to utilize the power of an FPGA cluster for face detection. …


On June 1, 2015 Intel and Altera announced , that they had entered into a definitive agreement under which Intel would acquire Altera for $16.7 billions. That was a major milestone for the FPGA community as Xilinx and Altera were the main FPGA vendors.

After the official announcement of AMD to acquire Xilinx today, there is a huge concern on the FPGA community on the future of FPGAs.

The main goal of the Xilinx acquisition is to create the industry’s leading high performance computing company, “significantly expanding the breadth of AMD’s product portfolio and customer set across diverse growth markets…


Evaluating the best hardware platform for your deep learning application can sometimes be challenging. Also, the marketing numbers provided by several companies sometimes can be misleading as they refer to specialized benchmarks. MLPerf’s mission is to build fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services. A widely accepted benchmark suite benefits the entire community, including developers, manufacturers, machine learning engineers, application providers, and end users. MLPerf provides a standard based benchmark that allows the performance comparison of several hardware platforms.

MLPerf 0.7 Inference results on a cluster of 2 Alveo U250 FPGA cards using InAccel orchestrator

InAccel has released today the results for the Deep Learning inference on…


FPGAs have been emerged as a powerful accelerator platform for many applications like deep neural networks, machine learning and video transcoding. FPGA vendors have released recently powerful FPGAs that can provide higher throughput, lower latency and better energy efficiency compared to GPUs. Cloud vendors have recognized the power of FPGAs and have recently adopted FPGAs as a computing resource that users can utilize. AWS was the first cloud provider that offered FPGAs as a possible computing resource for the users.

Enterprises that want to utilize the power of FPGAs are often puzzled whether it is most efficient and cost efficient…

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