InAccel provides FPGA-based accelerators specifically designed for machine learning analytics, significantly increasing data processing speed and efficiency. Their technology enables organizations to perform complex analytics tasks more rapidly, facilitating quicker insights from large datasets.
Funding
$600K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Modern data analytics and machine learning tasks require significant computational resources, leading to long processing times and high energy consumption, especially when dealing with large datasets. Existing solutions often lack the flexibility to adapt to diverse workloads and deployment environments, hindering efficient resource utilization.
Solution
InAccel provides application acceleration solutions based on field-programmable gate arrays (FPGAs) to enhance the performance and efficiency of data-intensive applications. Their technology offers a comprehensive ecosystem for deploying, scaling, orchestrating, and monitoring FPGA-based accelerators across various environments, including on-premises, cloud, and hybrid infrastructures. The Coral framework abstracts away the complexities of FPGA resource management, enabling developers to focus on application development while benefiting from hardware acceleration. InAccel's solutions facilitate faster execution, reduced costs, and improved resource utilization for computationally intensive tasks.
Target Audience
InAccel targets data scientists, machine learning engineers, and software developers who require high-performance computing solutions for data analytics, genomics, financial modeling, computer vision, and other computationally intensive applications.
Features
- FPGA IP Cores: Ready-to-use FPGA accelerators, primarily targeting machine learning domains.
- Bitstream Repository: An end-to-end solution for managing the deployment lifecycle of FPGA binaries, including versioning and distribution.
- Coral Resource Manager: A framework for distributed acceleration of large datasets across clusters of FPGA resources, using simple programming models.
- Coral Monitor: A real-time monitoring tool providing insights into power, thermal, and structural information for FPGAs and GPUs.
- Support for deployment in Docker, Kubernetes, Podman, and Singularity environments.
- FPGA-accelerated machine learning in Python using scikit-learn.
- Seamless acceleration of Spark ML pipelines.
- Integration with Kubesphere for multi-tenant, enterprise-grade FPGA cluster deployment.