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How FPGAs can be deployed in data centers efficiently

The FPGA operator allows cluster admins to manage their remote FPGA-powered servers the same way they manage CPU-based systems, but also regular users to target particular FPGA types and explicitly consume FPGA resources in their workloads. This makes it easy to bring up a fleet of remote systems and run accelerated applications without additional technical expertise on the ground.

How better FPGA resource utilization can save you time and money

Hardware accelerators, like GPUs and FPGAs, offer much higher computing power compared to typical processors. This is the reason that more and more cloud providers offer FPGAs and GPUs as computing resources. However, they are among the most expensive devices in a datacenter, thus being very important to make sure that all of the resources in a system are being shared and as fully utilized as possible

How Machine Learning can help reduce the road accidents

SafeDisplay automatically recognize if motorbike riders wear helmets and display the information on the panel to encourage the use of helmets.

Easy deployment and scaling of Vitis AI accelerators using InAccel

To ease the deployment, scaling, and management of Vitis AI applications, InAccel develops the InAccel Vitis AI runtime, which is an abstraction layer, that hides the integration complexity from the software developers and ML engineers, but also simplifies the deployment and maintenance of FPGA-powered AI services. It also offers FPGA provisioning and auto-scaling capabilities that can meet every production-grade requirement.

The main advantage of this approach is that it allows instant multi-cloud FPGA deployment as the same application can be deployed on several node on the clouds and the FPGA artifact manager will be used to configure the FPGA with the right bitstream file.

Scaling Performance of DNN Inference in FPGA clusters using InAccel orchestrator

How Xilinx used InAccel Coral for the evaluation of Multi-FPGA deployment for DNN. The MLPerf evaluation on multi-FPGA accelerators has been done using the InAccel Coral orchestrator and resource manager.

GPUs vs FPGAs: Which one is better in DL and Data Centers applications

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?

Simplify AI Deployment at scale on FPGA clusters

InAccel provides a unique FPGA cluster manager that allows easy of deployment and scaling at production level for AI and ML applications. The FPGA manager can be used both for inference and training of ML models.

FPGA-accelerated ML on MindsDB's Lightwood

InAccel provides the Logistic Regression accelerator which can accelerate the training time of a machine learning model and it is available for Intel's Arria10 FPGAs, Amazon EC2 F1 instances and Xilinx's Alveo U200, U250 and U280 FPGA platforms. So we developed a LogisticRegression class that can be selected as the desired mixer to create the Lightwood Predictor.

What is missing for the success of the AI HW chips

The latest version of the Coral resource manager allows the software community to instantiate and utilize a cluster of AI hardware accelerators with the same easy as invoking typical software functions. InAccel’s Coral resource manager allows multiple applications to share and utilize a cluster of accelerators in the same node (server) without worrying about the scheduling, load balancing and the resource management of each accelerator.

Inaccel announces record-breaking speed on facial detection test using an FPGA cluster

InAccel, a world-pioneer in the domain of FPGA-based accelerators, today released the results of a test in which a cluster of eight FPGAs in a single server were used to achieve 1266 fps in a facial detection speed test.

Accelerated Face detection on a cluster of 8 Alveo FPGA cards

InAccel has released today an integrated framework that allows to utilize the power of an FPGA cluster for face detection. Specifically, InAccel has presented a demo in which a cluster of 8 FPGAs are used to provide up to 1700 fps (supporting up to 56 cameras with 30 fps in a single server).

Accelerated MLPerf inference on FPGA clusters: InAccel unique solution

Scaling out Inspur’s TF2 Deep Learning Inference engine

In this case we show how InAccel orchestrator can be integrated with TF2 to allow multi-tenant scalable deployment of TF2 to a cluster of Intel-based FPGAs. We show how InAccel’s orchestrator allows easy deployment, scaling, resource management, and task scheduling for FPGAs making it easier than ever, the deployment and the utilization of FPGA for Deep Learning Inference.

Hybrid, Heterogeneous auto-scalable FPGA deployment

Enterprise customers now have the option to adopt a hybrid deployment for their FPGA-accelerated applications. That means, that companies can purchase a small number of FPGAs deployed on-prem and if these FPGAs are overloaded, they can automatically scale it using the cloud resources. InAccel, using the unique platform-agnostic FPGA orchestration allows auto-scaling hybrid FPGA deployment in the most efficient way.

Accelerated Deep Learning Inference using FPGAs from your browser

Any user can now enjoy the speedup of the FPGA accelerators from their browser. In the DL example we show how users can enjoy much faster ResNet50 inference from the same Keras python notebook with zero code changes.

Deploying FPGAs over OpenShift for accelerated Machine Learning

InAccel, a pioneer on the domain of application accelerations, has released today the easiest way to utilize FPGA-based accelerators over OpenShift. InAccel has developed a unique FPGA orchestrator that abstracts away the FPGA resources making much easier the deployment, scaling and automated resource management of the accelerators. Through the power of OpenShift, users can now deploy their applications on the cloud utilizing the power of FPGAs, easier than ever.

Accelerated Machine Learning from your browser

Data scientists and ML engineers can now speedup their applications from their browser using the power of FPGAs. ML developers can now run up to 15x faster their applications on Jupyter notebooks from their browser with zero code changes. ML developers are welcome to evaluate their applications for free on the new InAccel portal. InAccel, a pioneer in making FPGAs accessible and scalable, is providing the benefits of FPGA acceleration and utilization from your browser using the familiar Jupyter platform.

The need of an Accelerator Operating System

InAccel FPGA orchestrator abstracts away the available FPGA resources serving as an OS layer and “kubernetes-alike” layer for the applications that need to be deployed on FPGAs. The main advantages that InAccel’s orchestrator offers are easy deployment, automated resource management and instant scaling

Data scientists, ML engineers and HPC users can now speedup their applications using the power of FPGAs from their browser. InAccel has made available as a demo a framework that allow any user to instantly log in in the platform and start accessing the Xilinx Vitis™ libraries on a cluster of Alveo™ cards. The framework is built on top of Jupyter Hub.